{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 非线性偏微分方程的数据驱动解\n",
    "\n",
    "--------Data-driven Solutions of Nonlinear Partial Differential Equations\n",
    "\n",
    "在这篇分为两部分的论文的[第一部分](https://arxiv.org/abs/1711.10561)中，我们将重点讨论计算**一般形式偏微分方程**(partial differential equations of the general form)的数据驱动解\n",
    "$$\n",
    "\\large u_t + \\cal N[u] = 0, x\\in Ω, t \\in [0,T],\n",
    "$$\n",
    "其中 $u(t,x)$ 表示潜在（隐藏）解，$\\cal N[⋅]$ 是一个**非线性微分算子**(nonlinear differential operator)，$Ω$ 是 $R^D$ 的子集。接下来，我们提出了两类不同的算法，即**连续时间模型和离散时间模型**(continuous and discrete time models)，并通过不同的基准问题来突出它们的性质和性能。[这里](https://github.com/maziarraissi/PINNs)提供了所有代码和数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 连续时间模型\n",
    "\n",
    "--------Continuous Time Models\n",
    "\n",
    "我们定义 $f(t,x)$ 为\n",
    "$$\n",
    "\\large f := u_t + \\cal N[u],\n",
    "$$\n",
    "\n",
    "然后用深度神经网络逼近 $u(t,x)$ 。这个假设产生了一个[物理信息神经网络](https://arxiv.org/abs/1711.10561) $f(t,x)$ 。这个网络可以通过计算图上的演算得到：[反向传播](http://colah.github.io/posts/2015-08-Backprop/)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 示例1(Burgers方程)\n",
    "\n",
    "作为一个例子，让我们考虑[Burgers方程](https://en.wikipedia.org/wiki/Burgers'_equation)。在一维空间中，Burger方程的Dirichlet边界条件如下\n",
    "$$\n",
    "\\begin{align}\n",
    "& \\large u_t + uu_x - (0.01/\\pi)u_{xx} = 0, x \\in [-1,1], t \\in [0,1],\\\\\n",
    "& \\large u(0,x) = -sin(\\pi x),\\\\\n",
    "& \\large u(t,-1) = u(t,1) = 0.\\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "让我们定义 $f(t,x)$ 为\n",
    "$$\n",
    "\\large f := u_t + uu_x - (0.01/\\pi)u_{xx},\n",
    "$$\n",
    "\n",
    "然后用深度神经网络逼近 $u(t,x)$ 。为了强调这个想法的简单性，让我们用Python来实现它，只需要一点点[Tensorflow](https://www.tensorflow.org/)。为此， $u(t,x)$ 可以简单地定义为"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def u(t, x):\n",
    "    u = neural_net(tf.concat([t,x],1), weights, biases)\n",
    "    return u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相应地，[物理信息神经网络](https://arxiv.org/abs/1711.10561)$f(t,x)$ 采用以下形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(t, x):\n",
    "    u = u(t, x)\n",
    "    u_t = tf.gradients(u, t)[0]\n",
    "    u_x = tf.gradients(u, x)[0]\n",
    "    u_xx = tf.gradients(u_x, x)[0]\n",
    "    f = u_t + u*u_x - (0.01/tf.pi)*u_xx\n",
    "    return f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过最小化均方误差损失，可以学习神经网络 $u(t,x)$ 和 $f(t,x)$ 之间的共享参数\n",
    "$$\n",
    "\\begin{align}\n",
    "& MSE = MSE_u + MSF_f,\\\\\n",
    "& MSE_u = \\frac 1 {N_u} \\sum^{N_u}_{i=1} |u(t^i_u, x^i_u) - u^i|^2,\\\\\n",
    "& MSE_f = \\frac 1 {N_f} \\sum^{N_f}_{i=1} |f(t^i_f, x^i_f)|^2.\\\\\n",
    "\\end{align}\n",
    "$$\n",
    "这里，$\\{ {t^i_u, x^i_u, u^i} \\}^{N_u}_{i=1}$ 表示 $u(t,x)$ 上的初始和边界训练数据，$\\{{t^i_f,x^i_f}\\}^{N_f}_{i=1}$指定 $f(t,x)$ 的配置点。损失 $MSE_u$ 对应于初始和边界数据，而 $MSE_f$ 在有限的配置点集上强制执行Burgers方程施加的结构。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 代码源文件\n",
    "\n",
    "PINNs-master/appendix/continuous_time_identification%20(Burgers)/Burgers.py\n",
    "\n",
    "plotting.py 文件会修改matplotlib的latex源，如果电脑上未安装latex，别用plotting.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "@author: Maziar Raissi\n",
    "\"\"\"\n",
    "\n",
    "import sys\n",
    "# sys.path.insert(0, '../Utilities/')\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "# from plotting import newfig, savefig\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import matplotlib.gridspec as gridspec\n",
    "import time\n",
    "\n",
    "np.random.seed(1234)\n",
    "tf.set_random_seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class PhysicsInformedNN:\n",
    "    # Initialize the class\n",
    "    def __init__(self, X, u, layers, lb, ub):\n",
    "        \n",
    "        self.lb = lb\n",
    "        self.ub = ub\n",
    "        \n",
    "        self.x = X[:,0:1]\n",
    "        self.t = X[:,1:2]\n",
    "        self.u = u\n",
    "        \n",
    "        self.layers = layers\n",
    "        \n",
    "        # Initialize NNs\n",
    "        self.weights, self.biases = self.initialize_NN(layers)\n",
    "        \n",
    "        # tf placeholders and graph\n",
    "        self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,\n",
    "                                                     log_device_placement=True))\n",
    "        \n",
    "        # Initialize parameters\n",
    "        self.lambda_1 = tf.Variable([0.0], dtype=tf.float32)\n",
    "        self.lambda_2 = tf.Variable([-6.0], dtype=tf.float32)\n",
    "        \n",
    "        self.x_tf = tf.placeholder(tf.float32, shape=[None, self.x.shape[1]])\n",
    "        self.t_tf = tf.placeholder(tf.float32, shape=[None, self.t.shape[1]])\n",
    "        self.u_tf = tf.placeholder(tf.float32, shape=[None, self.u.shape[1]])\n",
    "                \n",
    "        self.u_pred = self.net_u(self.x_tf, self.t_tf)\n",
    "        self.f_pred = self.net_f(self.x_tf, self.t_tf)\n",
    "        \n",
    "        self.loss = tf.reduce_mean(tf.square(self.u_tf - self.u_pred)) + \\\n",
    "                    tf.reduce_mean(tf.square(self.f_pred))\n",
    "        \n",
    "        self.optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss, \n",
    "                                                                method = 'L-BFGS-B', \n",
    "                                                                options = {'maxiter': 50000,\n",
    "                                                                           'maxfun': 50000,\n",
    "                                                                           'maxcor': 50,\n",
    "                                                                           'maxls': 50,\n",
    "                                                                           'ftol' : 1.0 * np.finfo(float).eps})\n",
    "    \n",
    "        self.optimizer_Adam = tf.train.AdamOptimizer()\n",
    "        self.train_op_Adam = self.optimizer_Adam.minimize(self.loss)\n",
    "        \n",
    "        init = tf.global_variables_initializer()\n",
    "        self.sess.run(init)\n",
    "\n",
    "    def initialize_NN(self, layers):        \n",
    "        weights = []\n",
    "        biases = []\n",
    "        num_layers = len(layers) \n",
    "        for l in range(0,num_layers-1):\n",
    "            W = self.xavier_init(size=[layers[l], layers[l+1]])\n",
    "            b = tf.Variable(tf.zeros([1,layers[l+1]], dtype=tf.float32), dtype=tf.float32)\n",
    "            weights.append(W)\n",
    "            biases.append(b)        \n",
    "        return weights, biases\n",
    "        \n",
    "    def xavier_init(self, size):\n",
    "        in_dim = size[0]\n",
    "        out_dim = size[1]        \n",
    "        xavier_stddev = np.sqrt(2/(in_dim + out_dim))\n",
    "        return tf.Variable(tf.truncated_normal([in_dim, out_dim], stddev=xavier_stddev), dtype=tf.float32)\n",
    "    \n",
    "    def neural_net(self, X, weights, biases):\n",
    "        num_layers = len(weights) + 1\n",
    "        \n",
    "        H = 2.0*(X - self.lb)/(self.ub - self.lb) - 1.0\n",
    "        for l in range(0,num_layers-2):\n",
    "            W = weights[l]\n",
    "            b = biases[l]\n",
    "            H = tf.tanh(tf.add(tf.matmul(H, W), b))\n",
    "        W = weights[-1]\n",
    "        b = biases[-1]\n",
    "        Y = tf.add(tf.matmul(H, W), b)\n",
    "        return Y\n",
    "            \n",
    "    def net_u(self, x, t):  \n",
    "        u = self.neural_net(tf.concat([x,t],1), self.weights, self.biases)\n",
    "        return u\n",
    "    \n",
    "    def net_f(self, x, t):\n",
    "        lambda_1 = self.lambda_1        \n",
    "        lambda_2 = tf.exp(self.lambda_2)\n",
    "        u = self.net_u(x,t)\n",
    "        u_t = tf.gradients(u, t)[0]\n",
    "        u_x = tf.gradients(u, x)[0]\n",
    "        u_xx = tf.gradients(u_x, x)[0]\n",
    "        f = u_t + lambda_1*u*u_x - lambda_2*u_xx\n",
    "        \n",
    "        return f\n",
    "    \n",
    "    def callback(self, loss, lambda_1, lambda_2):\n",
    "        print('Loss: %e, l1: %.5f, l2: %.5f' % (loss, lambda_1, np.exp(lambda_2)))\n",
    "        \n",
    "        \n",
    "    def train(self, nIter):\n",
    "        tf_dict = {self.x_tf: self.x, self.t_tf: self.t, self.u_tf: self.u}\n",
    "        \n",
    "        start_time = time.time()\n",
    "        for it in range(nIter):\n",
    "            self.sess.run(self.train_op_Adam, tf_dict)\n",
    "            \n",
    "            # Print\n",
    "            if it % 10 == 0:\n",
    "                elapsed = time.time() - start_time\n",
    "                loss_value = self.sess.run(self.loss, tf_dict)\n",
    "                lambda_1_value = self.sess.run(self.lambda_1)\n",
    "                lambda_2_value = np.exp(self.sess.run(self.lambda_2))\n",
    "                print('It: %d, Loss: %.3e, Lambda_1: %.3f, Lambda_2: %.6f, Time: %.2f' % \n",
    "                      (it, loss_value, lambda_1_value, lambda_2_value, elapsed))\n",
    "                start_time = time.time()\n",
    "        \n",
    "        self.optimizer.minimize(self.sess,\n",
    "                                feed_dict = tf_dict,\n",
    "                                fetches = [self.loss, self.lambda_1, self.lambda_2],\n",
    "                                loss_callback = self.callback)\n",
    "        \n",
    "        \n",
    "    def predict(self, X_star):\n",
    "        \n",
    "        tf_dict = {self.x_tf: X_star[:,0:1], self.t_tf: X_star[:,1:2]}\n",
    "        \n",
    "        u_star = self.sess.run(self.u_pred, tf_dict)\n",
    "        f_star = self.sess.run(self.f_pred, tf_dict)\n",
    "        \n",
    "        return u_star, f_star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device mapping:\n",
      "\n",
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "Loss: 3.949374e-01, l1: 0.00000, l2: 0.00248\n",
      "Loss: 7.736639e-01, l1: 0.00000, l2: 0.00248\n",
      "Loss: 3.374787e-01, l1: 0.00000, l2: 0.00248\n",
      "Loss: 2.880107e-01, l1: 0.00016, l2: 0.00248\n",
      "Loss: 8.284917e+00, l1: 0.00138, l2: 0.00248\n",
      "Loss: 2.714846e-01, l1: 0.00022, l2: 0.00248\n",
      "Loss: 2.565792e-01, l1: 0.00035, l2: 0.00248\n",
      "Loss: 2.386290e-01, l1: 0.00033, l2: 0.00248\n",
      "Loss: 2.358943e-01, l1: 0.00035, l2: 0.00248\n",
      "Loss: 2.344091e-01, l1: 0.00035, l2: 0.00248\n",
      "Loss: 2.298073e-01, l1: 0.00035, l2: 0.00248\n",
      "Loss: 2.098748e-01, l1: 0.00028, l2: 0.00248\n",
      "Loss: 2.486481e-01, l1: -0.00004, l2: 0.00248\n",
      "Loss: 1.964649e-01, l1: 0.00017, l2: 0.00248\n",
      "Loss: 1.716983e-01, l1: 0.00061, l2: 0.00248\n",
      "Loss: 1.127580e-01, l1: 0.00481, l2: 0.00248\n",
      "Loss: 1.011994e-01, l1: 0.01673, l2: 0.00247\n",
      "Loss: 9.693976e-02, l1: 0.02580, l2: 0.00247\n",
      "Loss: 9.442923e-02, l1: 0.03720, l2: 0.00247\n",
      "Loss: 8.520013e-02, l1: 0.10101, l2: 0.00244\n",
      "Loss: 3.225443e-01, l1: 0.41721, l2: 0.00231\n",
      "Loss: 7.502717e-02, l1: 0.18143, l2: 0.00241\n",
      "Loss: 1.585865e-01, l1: 0.36336, l2: 0.00233\n",
      "Loss: 6.990007e-02, l1: 0.22599, l2: 0.00239\n",
      "Loss: 6.466994e-02, l1: 0.23109, l2: 0.00239\n",
      "Loss: 6.185767e-02, l1: 0.20459, l2: 0.00239\n",
      "Loss: 5.723807e-02, l1: 0.17932, l2: 0.00240\n",
      "Loss: 5.315698e-02, l1: 0.16877, l2: 0.00240\n",
      "Loss: 4.721127e-02, l1: 0.09504, l2: 0.00242\n",
      "Loss: 4.384773e-02, l1: 0.10597, l2: 0.00241\n",
      "Loss: 3.857749e-02, l1: 0.10928, l2: 0.00241\n",
      "Loss: 3.693525e-02, l1: 0.08998, l2: 0.00241\n",
      "Loss: 3.529328e-02, l1: 0.09171, l2: 0.00241\n",
      "Loss: 3.481431e-02, l1: 0.08673, l2: 0.00241\n",
      "Loss: 3.451390e-02, l1: 0.08282, l2: 0.00241\n",
      "Loss: 3.379339e-02, l1: 0.07894, l2: 0.00241\n",
      "Loss: 3.336038e-02, l1: 0.08336, l2: 0.00241\n",
      "Loss: 3.287323e-02, l1: 0.08174, l2: 0.00241\n",
      "Loss: 3.246094e-02, l1: 0.08479, l2: 0.00241\n",
      "Loss: 3.122701e-02, l1: 0.08696, l2: 0.00241\n",
      "Loss: 3.083479e-02, l1: 0.08418, l2: 0.00242\n",
      "Loss: 3.051556e-02, l1: 0.08457, l2: 0.00242\n",
      "Loss: 3.041350e-02, l1: 0.08593, l2: 0.00242\n",
      "Loss: 2.982197e-02, l1: 0.09180, l2: 0.00242\n",
      "Loss: 2.902483e-02, l1: 0.10620, l2: 0.00241\n",
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      "Loss: 2.791499e-02, l1: 0.13170, l2: 0.00241\n",
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      "Loss: 2.534775e-02, l1: 0.15912, l2: 0.00243\n",
      "Loss: 2.480152e-02, l1: 0.17271, l2: 0.00243\n",
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      "Loss: 2.395696e-02, l1: 0.19740, l2: 0.00243\n",
      "Loss: 2.371206e-02, l1: 0.20169, l2: 0.00243\n",
      "Loss: 2.354506e-02, l1: 0.19572, l2: 0.00244\n",
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      "Loss: 2.236641e-02, l1: 0.20007, l2: 0.00244\n",
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      "Loss: 2.208503e-02, l1: 0.20187, l2: 0.00244\n",
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      "Loss: 2.198828e-02, l1: 0.20134, l2: 0.00244\n",
      "Loss: 2.192090e-02, l1: 0.20042, l2: 0.00244\n",
      "Loss: 2.181284e-02, l1: 0.20089, l2: 0.00244\n",
      "Loss: 2.149835e-02, l1: 0.20332, l2: 0.00245\n",
      "Loss: 2.103609e-02, l1: 0.21474, l2: 0.00246\n",
      "Loss: 2.043841e-02, l1: 0.22753, l2: 0.00247\n",
      "Loss: 1.992211e-02, l1: 0.25617, l2: 0.00247\n",
      "Loss: 1.996094e-02, l1: 0.25714, l2: 0.00247\n",
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      "Loss: 1.964681e-02, l1: 0.24997, l2: 0.00247\n",
      "Loss: 1.954489e-02, l1: 0.25151, l2: 0.00247\n",
      "Loss: 1.943782e-02, l1: 0.25559, l2: 0.00247\n",
      "Loss: 1.913744e-02, l1: 0.26363, l2: 0.00248\n",
      "Loss: 1.862067e-02, l1: 0.28254, l2: 0.00250\n",
      "Loss: 1.790436e-02, l1: 0.30951, l2: 0.00253\n",
      "Loss: 1.751213e-02, l1: 0.32635, l2: 0.00254\n",
      "Loss: 1.704628e-02, l1: 0.30842, l2: 0.00254\n",
      "Loss: 1.674539e-02, l1: 0.31688, l2: 0.00254\n",
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Loss: 1.734121e-05, l1: 0.99824, l2: 0.00320\n",
      "Loss: 1.729861e-05, l1: 0.99814, l2: 0.00320\n",
      "Loss: 1.728717e-05, l1: 0.99809, l2: 0.00320\n",
      "Loss: 1.726184e-05, l1: 0.99803, l2: 0.00320\n",
      "Loss: 1.724267e-05, l1: 0.99802, l2: 0.00320\n",
      "Loss: 1.723951e-05, l1: 0.99794, l2: 0.00320\n",
      "Loss: 1.722357e-05, l1: 0.99798, l2: 0.00320\n",
      "Loss: 1.721041e-05, l1: 0.99803, l2: 0.00320\n",
      "Loss: 1.718712e-05, l1: 0.99811, l2: 0.00320\n",
      "Loss: 1.717610e-05, l1: 0.99820, l2: 0.00320\n",
      "Loss: 1.716634e-05, l1: 0.99822, l2: 0.00320\n",
      "Loss: 1.716050e-05, l1: 0.99821, l2: 0.00320\n",
      "Loss: 1.718453e-05, l1: 0.99812, l2: 0.00320\n",
      "Loss: 1.715633e-05, l1: 0.99819, l2: 0.00320\n",
      "Loss: 1.714872e-05, l1: 0.99819, l2: 0.00320\n",
      "Loss: 1.713703e-05, l1: 0.99819, l2: 0.00320\n",
      "Loss: 1.712768e-05, l1: 0.99820, l2: 0.00320\n",
      "Loss: 1.711666e-05, l1: 0.99821, l2: 0.00320\n",
      "Loss: 1.710005e-05, l1: 0.99824, l2: 0.00320\n",
      "Loss: 1.708955e-05, l1: 0.99820, l2: 0.00320\n",
      "Loss: 1.708307e-05, l1: 0.99821, l2: 0.00320\n",
      "Loss: 1.707033e-05, l1: 0.99819, l2: 0.00320\n",
      "Loss: 1.706079e-05, l1: 0.99823, l2: 0.00320\n",
      "Loss: 1.705008e-05, l1: 0.99821, l2: 0.00320\n",
      "Loss: 1.703646e-05, l1: 0.99818, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.704580e-05, l1: 0.99820, l2: 0.00320\n",
      "Loss: 1.703312e-05, l1: 0.99817, l2: 0.00320\n",
      "Loss: 1.703325e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703334e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703313e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703320e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703279e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "Loss: 1.703264e-05, l1: 0.99816, l2: 0.00320\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
      "  Objective function value: 0.000017\n",
      "  Number of iterations: 2565\n",
      "  Number of functions evaluations: 2841\n",
      "Error u: 2.640229e-03\n",
      "Error l1: 0.18408%\n",
      "Error l2: 0.61554%\n",
      "Device mapping:\n",
      "\n",
      "It: 0, Loss: 4.023e-01, Lambda_1: 0.001, Lambda_2: 0.002480, Time: 0.70\n",
      "It: 10, Loss: 3.000e-01, Lambda_1: -0.003, Lambda_2: 0.002487, Time: 0.11\n",
      "It: 20, Loss: 2.598e-01, Lambda_1: -0.008, Lambda_2: 0.002508, Time: 0.11\n",
      "It: 30, Loss: 2.482e-01, Lambda_1: -0.020, Lambda_2: 0.002537, Time: 0.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 40, Loss: 2.436e-01, Lambda_1: -0.032, Lambda_2: 0.002571, Time: 0.11\n",
      "It: 50, Loss: 2.349e-01, Lambda_1: -0.044, Lambda_2: 0.002605, Time: 0.11\n",
      "It: 60, Loss: 2.216e-01, Lambda_1: -0.055, Lambda_2: 0.002642, Time: 0.11\n",
      "It: 70, Loss: 1.944e-01, Lambda_1: -0.064, Lambda_2: 0.002676, Time: 0.11\n",
      "It: 80, Loss: 1.461e-01, Lambda_1: -0.067, Lambda_2: 0.002683, Time: 0.11\n",
      "It: 90, Loss: 9.770e-02, Lambda_1: -0.060, Lambda_2: 0.002638, Time: 0.11\n",
      "It: 100, Loss: 7.170e-02, Lambda_1: -0.041, Lambda_2: 0.002582, Time: 0.12\n",
      "It: 110, Loss: 5.985e-02, Lambda_1: -0.018, Lambda_2: 0.002524, Time: 0.11\n",
      "It: 120, Loss: 5.041e-02, Lambda_1: 0.005, Lambda_2: 0.002468, Time: 0.11\n",
      "It: 130, Loss: 4.353e-02, Lambda_1: 0.024, Lambda_2: 0.002422, Time: 0.11\n",
      "It: 140, Loss: 4.003e-02, Lambda_1: 0.039, Lambda_2: 0.002393, Time: 0.11\n",
      "It: 150, Loss: 3.852e-02, Lambda_1: 0.048, Lambda_2: 0.002380, Time: 0.11\n",
      "It: 160, Loss: 3.769e-02, Lambda_1: 0.051, Lambda_2: 0.002377, Time: 0.11\n",
      "It: 170, Loss: 3.704e-02, Lambda_1: 0.052, Lambda_2: 0.002380, Time: 0.11\n",
      "It: 180, Loss: 3.649e-02, Lambda_1: 0.052, Lambda_2: 0.002385, Time: 0.11\n",
      "It: 190, Loss: 3.602e-02, Lambda_1: 0.052, Lambda_2: 0.002390, Time: 0.11\n",
      "It: 200, Loss: 3.560e-02, Lambda_1: 0.053, Lambda_2: 0.002395, Time: 0.12\n",
      "It: 210, Loss: 3.521e-02, Lambda_1: 0.054, Lambda_2: 0.002398, Time: 0.11\n",
      "It: 220, Loss: 3.483e-02, Lambda_1: 0.055, Lambda_2: 0.002402, Time: 0.11\n",
      "It: 230, Loss: 3.445e-02, Lambda_1: 0.057, Lambda_2: 0.002405, Time: 0.11\n",
      "It: 240, Loss: 3.410e-02, Lambda_1: 0.059, Lambda_2: 0.002409, Time: 0.11\n",
      "It: 250, Loss: 3.369e-02, Lambda_1: 0.060, Lambda_2: 0.002413, Time: 0.12\n",
      "It: 260, Loss: 3.329e-02, Lambda_1: 0.062, Lambda_2: 0.002417, Time: 0.11\n",
      "It: 270, Loss: 3.287e-02, Lambda_1: 0.064, Lambda_2: 0.002421, Time: 0.11\n",
      "It: 280, Loss: 3.244e-02, Lambda_1: 0.066, Lambda_2: 0.002425, Time: 0.11\n",
      "It: 290, Loss: 3.198e-02, Lambda_1: 0.068, Lambda_2: 0.002430, Time: 0.11\n",
      "It: 300, Loss: 3.150e-02, Lambda_1: 0.070, Lambda_2: 0.002435, Time: 0.11\n",
      "It: 310, Loss: 3.099e-02, Lambda_1: 0.073, Lambda_2: 0.002440, Time: 0.11\n",
      "It: 320, Loss: 3.047e-02, Lambda_1: 0.076, Lambda_2: 0.002445, Time: 0.11\n",
      "It: 330, Loss: 2.993e-02, Lambda_1: 0.079, Lambda_2: 0.002451, Time: 0.11\n",
      "It: 340, Loss: 2.938e-02, Lambda_1: 0.082, Lambda_2: 0.002457, Time: 0.11\n",
      "It: 350, Loss: 2.882e-02, Lambda_1: 0.085, Lambda_2: 0.002464, Time: 0.11\n",
      "It: 360, Loss: 2.826e-02, Lambda_1: 0.089, Lambda_2: 0.002470, Time: 0.11\n",
      "It: 370, Loss: 2.770e-02, Lambda_1: 0.094, Lambda_2: 0.002478, Time: 0.11\n",
      "It: 380, Loss: 2.714e-02, Lambda_1: 0.098, Lambda_2: 0.002485, Time: 0.11\n",
      "It: 390, Loss: 2.660e-02, Lambda_1: 0.103, Lambda_2: 0.002492, Time: 0.11\n",
      "It: 400, Loss: 2.607e-02, Lambda_1: 0.108, Lambda_2: 0.002499, Time: 0.11\n",
      "It: 410, Loss: 2.556e-02, Lambda_1: 0.113, Lambda_2: 0.002506, Time: 0.11\n",
      "It: 420, Loss: 2.507e-02, Lambda_1: 0.119, Lambda_2: 0.002513, Time: 0.11\n",
      "It: 430, Loss: 2.459e-02, Lambda_1: 0.124, Lambda_2: 0.002519, Time: 0.11\n",
      "It: 440, Loss: 2.413e-02, Lambda_1: 0.130, Lambda_2: 0.002525, Time: 0.11\n",
      "It: 450, Loss: 2.369e-02, Lambda_1: 0.136, Lambda_2: 0.002531, Time: 0.11\n",
      "It: 460, Loss: 2.341e-02, Lambda_1: 0.142, Lambda_2: 0.002536, Time: 0.11\n",
      "It: 470, Loss: 2.287e-02, Lambda_1: 0.148, Lambda_2: 0.002542, Time: 0.11\n",
      "It: 480, Loss: 2.248e-02, Lambda_1: 0.154, Lambda_2: 0.002547, Time: 0.11\n",
      "It: 490, Loss: 2.211e-02, Lambda_1: 0.160, Lambda_2: 0.002553, Time: 0.11\n",
      "It: 500, Loss: 2.172e-02, Lambda_1: 0.166, Lambda_2: 0.002559, Time: 0.11\n",
      "It: 510, Loss: 2.138e-02, Lambda_1: 0.172, Lambda_2: 0.002565, Time: 0.11\n",
      "It: 520, Loss: 2.122e-02, Lambda_1: 0.178, Lambda_2: 0.002570, Time: 0.11\n",
      "It: 530, Loss: 2.078e-02, Lambda_1: 0.184, Lambda_2: 0.002576, Time: 0.11\n",
      "It: 540, Loss: 2.035e-02, Lambda_1: 0.191, Lambda_2: 0.002582, Time: 0.11\n",
      "It: 550, Loss: 2.002e-02, Lambda_1: 0.197, Lambda_2: 0.002588, Time: 0.11\n",
      "It: 560, Loss: 1.970e-02, Lambda_1: 0.203, Lambda_2: 0.002594, Time: 0.11\n",
      "It: 570, Loss: 1.945e-02, Lambda_1: 0.210, Lambda_2: 0.002600, Time: 0.11\n",
      "It: 580, Loss: 1.913e-02, Lambda_1: 0.216, Lambda_2: 0.002606, Time: 0.11\n",
      "It: 590, Loss: 1.876e-02, Lambda_1: 0.223, Lambda_2: 0.002612, Time: 0.11\n",
      "It: 600, Loss: 1.839e-02, Lambda_1: 0.230, Lambda_2: 0.002618, Time: 0.11\n",
      "It: 610, Loss: 1.807e-02, Lambda_1: 0.236, Lambda_2: 0.002624, Time: 0.11\n",
      "It: 620, Loss: 1.774e-02, Lambda_1: 0.243, Lambda_2: 0.002631, Time: 0.11\n",
      "It: 630, Loss: 1.761e-02, Lambda_1: 0.250, Lambda_2: 0.002637, Time: 0.11\n",
      "It: 640, Loss: 1.743e-02, Lambda_1: 0.258, Lambda_2: 0.002644, Time: 0.11\n",
      "It: 650, Loss: 1.687e-02, Lambda_1: 0.265, Lambda_2: 0.002651, Time: 0.11\n",
      "It: 660, Loss: 1.655e-02, Lambda_1: 0.272, Lambda_2: 0.002660, Time: 0.11\n",
      "It: 670, Loss: 1.626e-02, Lambda_1: 0.279, Lambda_2: 0.002669, Time: 0.11\n",
      "It: 680, Loss: 1.597e-02, Lambda_1: 0.286, Lambda_2: 0.002678, Time: 0.11\n",
      "It: 690, Loss: 1.571e-02, Lambda_1: 0.293, Lambda_2: 0.002688, Time: 0.11\n",
      "It: 700, Loss: 1.583e-02, Lambda_1: 0.300, Lambda_2: 0.002699, Time: 0.11\n",
      "It: 710, Loss: 1.511e-02, Lambda_1: 0.307, Lambda_2: 0.002710, Time: 0.11\n",
      "It: 720, Loss: 1.489e-02, Lambda_1: 0.314, Lambda_2: 0.002723, Time: 0.11\n",
      "It: 730, Loss: 1.459e-02, Lambda_1: 0.321, Lambda_2: 0.002736, Time: 0.11\n",
      "It: 740, Loss: 1.429e-02, Lambda_1: 0.328, Lambda_2: 0.002750, Time: 0.11\n",
      "It: 750, Loss: 1.456e-02, Lambda_1: 0.335, Lambda_2: 0.002764, Time: 0.11\n",
      "It: 760, Loss: 1.397e-02, Lambda_1: 0.342, Lambda_2: 0.002778, Time: 0.11\n",
      "It: 770, Loss: 1.342e-02, Lambda_1: 0.348, Lambda_2: 0.002794, Time: 0.12\n",
      "It: 780, Loss: 1.314e-02, Lambda_1: 0.355, Lambda_2: 0.002812, Time: 0.11\n",
      "It: 790, Loss: 1.292e-02, Lambda_1: 0.362, Lambda_2: 0.002828, Time: 0.11\n",
      "It: 800, Loss: 1.297e-02, Lambda_1: 0.369, Lambda_2: 0.002844, Time: 0.12\n",
      "It: 810, Loss: 1.242e-02, Lambda_1: 0.376, Lambda_2: 0.002860, Time: 0.11\n",
      "It: 820, Loss: 1.202e-02, Lambda_1: 0.383, Lambda_2: 0.002877, Time: 0.11\n",
      "It: 830, Loss: 1.214e-02, Lambda_1: 0.390, Lambda_2: 0.002894, Time: 0.11\n",
      "It: 840, Loss: 1.159e-02, Lambda_1: 0.397, Lambda_2: 0.002910, Time: 0.11\n",
      "It: 850, Loss: 1.130e-02, Lambda_1: 0.404, Lambda_2: 0.002924, Time: 0.13\n",
      "It: 860, Loss: 1.118e-02, Lambda_1: 0.411, Lambda_2: 0.002940, Time: 0.13\n",
      "It: 870, Loss: 1.083e-02, Lambda_1: 0.418, Lambda_2: 0.002952, Time: 0.12\n",
      "It: 880, Loss: 1.137e-02, Lambda_1: 0.425, Lambda_2: 0.002964, Time: 0.11\n",
      "It: 890, Loss: 1.291e-02, Lambda_1: 0.431, Lambda_2: 0.002976, Time: 0.11\n",
      "It: 900, Loss: 1.183e-02, Lambda_1: 0.437, Lambda_2: 0.002982, Time: 0.11\n",
      "It: 910, Loss: 1.241e-02, Lambda_1: 0.441, Lambda_2: 0.002993, Time: 0.11\n",
      "It: 920, Loss: 1.343e-02, Lambda_1: 0.443, Lambda_2: 0.003004, Time: 0.11\n",
      "It: 930, Loss: 1.026e-02, Lambda_1: 0.446, Lambda_2: 0.003007, Time: 0.11\n",
      "It: 940, Loss: 1.104e-02, Lambda_1: 0.449, Lambda_2: 0.003018, Time: 0.11\n",
      "It: 950, Loss: 9.790e-03, Lambda_1: 0.451, Lambda_2: 0.003029, Time: 0.11\n",
      "It: 960, Loss: 9.576e-03, Lambda_1: 0.455, Lambda_2: 0.003041, Time: 0.11\n",
      "It: 970, Loss: 9.349e-03, Lambda_1: 0.460, Lambda_2: 0.003048, Time: 0.11\n",
      "It: 980, Loss: 9.189e-03, Lambda_1: 0.466, Lambda_2: 0.003055, Time: 0.11\n",
      "It: 990, Loss: 9.044e-03, Lambda_1: 0.471, Lambda_2: 0.003062, Time: 0.11\n",
      "It: 1000, Loss: 8.904e-03, Lambda_1: 0.476, Lambda_2: 0.003070, Time: 0.11\n",
      "It: 1010, Loss: 8.770e-03, Lambda_1: 0.481, Lambda_2: 0.003077, Time: 0.12\n",
      "It: 1020, Loss: 8.640e-03, Lambda_1: 0.486, Lambda_2: 0.003086, Time: 0.13\n",
      "It: 1030, Loss: 8.513e-03, Lambda_1: 0.491, Lambda_2: 0.003094, Time: 0.11\n",
      "It: 1040, Loss: 8.389e-03, Lambda_1: 0.496, Lambda_2: 0.003102, Time: 0.11\n",
      "It: 1050, Loss: 8.268e-03, Lambda_1: 0.501, Lambda_2: 0.003110, Time: 0.13\n",
      "It: 1060, Loss: 8.150e-03, Lambda_1: 0.505, Lambda_2: 0.003118, Time: 0.12\n",
      "It: 1070, Loss: 8.034e-03, Lambda_1: 0.510, Lambda_2: 0.003127, Time: 0.11\n",
      "It: 1080, Loss: 7.920e-03, Lambda_1: 0.515, Lambda_2: 0.003135, Time: 0.11\n",
      "It: 1090, Loss: 7.808e-03, Lambda_1: 0.519, Lambda_2: 0.003144, Time: 0.12\n",
      "It: 1100, Loss: 7.699e-03, Lambda_1: 0.524, Lambda_2: 0.003153, Time: 0.12\n",
      "It: 1110, Loss: 7.592e-03, Lambda_1: 0.528, Lambda_2: 0.003162, Time: 0.11\n",
      "It: 1120, Loss: 7.486e-03, Lambda_1: 0.532, Lambda_2: 0.003170, Time: 0.11\n",
      "It: 1130, Loss: 7.383e-03, Lambda_1: 0.537, Lambda_2: 0.003179, Time: 0.11\n",
      "It: 1140, Loss: 7.281e-03, Lambda_1: 0.541, Lambda_2: 0.003188, Time: 0.11\n",
      "It: 1150, Loss: 7.181e-03, Lambda_1: 0.545, Lambda_2: 0.003197, Time: 0.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 1160, Loss: 7.082e-03, Lambda_1: 0.549, Lambda_2: 0.003206, Time: 0.13\n",
      "It: 1170, Loss: 6.985e-03, Lambda_1: 0.554, Lambda_2: 0.003215, Time: 0.11\n",
      "It: 1180, Loss: 6.889e-03, Lambda_1: 0.558, Lambda_2: 0.003225, Time: 0.11\n",
      "It: 1190, Loss: 6.796e-03, Lambda_1: 0.562, Lambda_2: 0.003234, Time: 0.11\n",
      "It: 1200, Loss: 6.915e-03, Lambda_1: 0.566, Lambda_2: 0.003242, Time: 0.13\n",
      "It: 1210, Loss: 6.653e-03, Lambda_1: 0.570, Lambda_2: 0.003250, Time: 0.13\n",
      "It: 1220, Loss: 1.763e-02, Lambda_1: 0.573, Lambda_2: 0.003255, Time: 0.11\n",
      "It: 1230, Loss: 1.003e-01, Lambda_1: 0.571, Lambda_2: 0.003200, Time: 0.13\n",
      "It: 1240, Loss: 4.025e-02, Lambda_1: 0.568, Lambda_2: 0.003182, Time: 0.11\n",
      "It: 1250, Loss: 2.189e-02, Lambda_1: 0.559, Lambda_2: 0.003192, Time: 0.11\n",
      "It: 1260, Loss: 1.354e-02, Lambda_1: 0.548, Lambda_2: 0.003204, Time: 0.11\n",
      "It: 1270, Loss: 9.321e-03, Lambda_1: 0.537, Lambda_2: 0.003231, Time: 0.11\n",
      "It: 1280, Loss: 8.257e-03, Lambda_1: 0.530, Lambda_2: 0.003252, Time: 0.11\n",
      "It: 1290, Loss: 7.612e-03, Lambda_1: 0.530, Lambda_2: 0.003261, Time: 0.13\n",
      "It: 1300, Loss: 7.434e-03, Lambda_1: 0.531, Lambda_2: 0.003268, Time: 0.11\n",
      "It: 1310, Loss: 7.268e-03, Lambda_1: 0.534, Lambda_2: 0.003274, Time: 0.11\n",
      "It: 1320, Loss: 7.153e-03, Lambda_1: 0.537, Lambda_2: 0.003279, Time: 0.11\n",
      "It: 1330, Loss: 7.057e-03, Lambda_1: 0.541, Lambda_2: 0.003283, Time: 0.11\n",
      "It: 1340, Loss: 6.971e-03, Lambda_1: 0.544, Lambda_2: 0.003287, Time: 0.11\n",
      "It: 1350, Loss: 6.889e-03, Lambda_1: 0.548, Lambda_2: 0.003292, Time: 0.11\n",
      "It: 1360, Loss: 6.811e-03, Lambda_1: 0.551, Lambda_2: 0.003297, Time: 0.11\n",
      "It: 1370, Loss: 6.737e-03, Lambda_1: 0.554, Lambda_2: 0.003302, Time: 0.11\n",
      "It: 1380, Loss: 6.666e-03, Lambda_1: 0.558, Lambda_2: 0.003307, Time: 0.11\n",
      "It: 1390, Loss: 6.597e-03, Lambda_1: 0.561, Lambda_2: 0.003313, Time: 0.11\n",
      "It: 1400, Loss: 6.530e-03, Lambda_1: 0.564, Lambda_2: 0.003319, Time: 0.11\n",
      "It: 1410, Loss: 6.465e-03, Lambda_1: 0.567, Lambda_2: 0.003324, Time: 0.11\n",
      "It: 1420, Loss: 6.401e-03, Lambda_1: 0.570, Lambda_2: 0.003330, Time: 0.11\n",
      "It: 1430, Loss: 6.339e-03, Lambda_1: 0.573, Lambda_2: 0.003336, Time: 0.11\n",
      "It: 1440, Loss: 6.278e-03, Lambda_1: 0.576, Lambda_2: 0.003342, Time: 0.13\n",
      "It: 1450, Loss: 6.218e-03, Lambda_1: 0.579, Lambda_2: 0.003348, Time: 0.13\n",
      "It: 1460, Loss: 6.159e-03, Lambda_1: 0.582, Lambda_2: 0.003354, Time: 0.11\n",
      "It: 1470, Loss: 6.101e-03, Lambda_1: 0.584, Lambda_2: 0.003361, Time: 0.11\n",
      "It: 1480, Loss: 6.044e-03, Lambda_1: 0.587, Lambda_2: 0.003367, Time: 0.11\n",
      "It: 1490, Loss: 5.988e-03, Lambda_1: 0.590, Lambda_2: 0.003373, Time: 0.11\n",
      "It: 1500, Loss: 5.932e-03, Lambda_1: 0.593, Lambda_2: 0.003380, Time: 0.11\n",
      "It: 1510, Loss: 5.876e-03, Lambda_1: 0.596, Lambda_2: 0.003386, Time: 0.11\n",
      "It: 1520, Loss: 5.822e-03, Lambda_1: 0.599, Lambda_2: 0.003393, Time: 0.11\n",
      "It: 1530, Loss: 5.768e-03, Lambda_1: 0.601, Lambda_2: 0.003399, Time: 0.11\n",
      "It: 1540, Loss: 5.714e-03, Lambda_1: 0.604, Lambda_2: 0.003406, Time: 0.11\n",
      "It: 1550, Loss: 5.661e-03, Lambda_1: 0.607, Lambda_2: 0.003412, Time: 0.11\n",
      "It: 1560, Loss: 5.608e-03, Lambda_1: 0.609, Lambda_2: 0.003419, Time: 0.11\n",
      "It: 1570, Loss: 5.556e-03, Lambda_1: 0.612, Lambda_2: 0.003426, Time: 0.11\n",
      "It: 1580, Loss: 5.504e-03, Lambda_1: 0.615, Lambda_2: 0.003432, Time: 0.11\n",
      "It: 1590, Loss: 5.452e-03, Lambda_1: 0.617, Lambda_2: 0.003439, Time: 0.11\n",
      "It: 1600, Loss: 5.401e-03, Lambda_1: 0.620, Lambda_2: 0.003446, Time: 0.11\n",
      "It: 1610, Loss: 5.350e-03, Lambda_1: 0.623, Lambda_2: 0.003453, Time: 0.11\n",
      "It: 1620, Loss: 5.299e-03, Lambda_1: 0.625, Lambda_2: 0.003460, Time: 0.11\n",
      "It: 1630, Loss: 5.248e-03, Lambda_1: 0.628, Lambda_2: 0.003466, Time: 0.11\n",
      "It: 1640, Loss: 5.198e-03, Lambda_1: 0.630, Lambda_2: 0.003473, Time: 0.11\n",
      "It: 1650, Loss: 5.148e-03, Lambda_1: 0.633, Lambda_2: 0.003480, Time: 0.11\n",
      "It: 1660, Loss: 5.099e-03, Lambda_1: 0.636, Lambda_2: 0.003487, Time: 0.11\n",
      "It: 1670, Loss: 5.049e-03, Lambda_1: 0.638, Lambda_2: 0.003494, Time: 0.11\n",
      "It: 1680, Loss: 5.000e-03, Lambda_1: 0.641, Lambda_2: 0.003501, Time: 0.11\n",
      "It: 1690, Loss: 4.951e-03, Lambda_1: 0.643, Lambda_2: 0.003508, Time: 0.11\n",
      "It: 1700, Loss: 4.902e-03, Lambda_1: 0.646, Lambda_2: 0.003515, Time: 0.11\n",
      "It: 1710, Loss: 4.853e-03, Lambda_1: 0.648, Lambda_2: 0.003522, Time: 0.11\n",
      "It: 1720, Loss: 4.804e-03, Lambda_1: 0.651, Lambda_2: 0.003530, Time: 0.11\n",
      "It: 1730, Loss: 4.756e-03, Lambda_1: 0.653, Lambda_2: 0.003537, Time: 0.11\n",
      "It: 1740, Loss: 4.707e-03, Lambda_1: 0.656, Lambda_2: 0.003544, Time: 0.11\n",
      "It: 1750, Loss: 4.659e-03, Lambda_1: 0.658, Lambda_2: 0.003551, Time: 0.11\n",
      "It: 1760, Loss: 4.611e-03, Lambda_1: 0.660, Lambda_2: 0.003558, Time: 0.11\n",
      "It: 1770, Loss: 4.563e-03, Lambda_1: 0.663, Lambda_2: 0.003566, Time: 0.11\n",
      "It: 1780, Loss: 4.515e-03, Lambda_1: 0.665, Lambda_2: 0.003573, Time: 0.11\n",
      "It: 1790, Loss: 4.467e-03, Lambda_1: 0.668, Lambda_2: 0.003580, Time: 0.11\n",
      "It: 1800, Loss: 4.419e-03, Lambda_1: 0.670, Lambda_2: 0.003587, Time: 0.11\n",
      "It: 1810, Loss: 4.372e-03, Lambda_1: 0.673, Lambda_2: 0.003595, Time: 0.11\n",
      "It: 1820, Loss: 4.324e-03, Lambda_1: 0.675, Lambda_2: 0.003602, Time: 0.11\n",
      "It: 1830, Loss: 4.277e-03, Lambda_1: 0.677, Lambda_2: 0.003610, Time: 0.11\n",
      "It: 1840, Loss: 4.229e-03, Lambda_1: 0.680, Lambda_2: 0.003617, Time: 0.11\n",
      "It: 1850, Loss: 4.182e-03, Lambda_1: 0.682, Lambda_2: 0.003624, Time: 0.11\n",
      "It: 1860, Loss: 4.135e-03, Lambda_1: 0.685, Lambda_2: 0.003632, Time: 0.11\n",
      "It: 1870, Loss: 4.088e-03, Lambda_1: 0.687, Lambda_2: 0.003639, Time: 0.11\n",
      "It: 1880, Loss: 4.042e-03, Lambda_1: 0.689, Lambda_2: 0.003647, Time: 0.12\n",
      "It: 1890, Loss: 3.995e-03, Lambda_1: 0.692, Lambda_2: 0.003654, Time: 0.11\n",
      "It: 1900, Loss: 3.948e-03, Lambda_1: 0.694, Lambda_2: 0.003662, Time: 0.11\n",
      "It: 1910, Loss: 3.902e-03, Lambda_1: 0.696, Lambda_2: 0.003669, Time: 0.11\n",
      "It: 1920, Loss: 3.856e-03, Lambda_1: 0.699, Lambda_2: 0.003677, Time: 0.11\n",
      "It: 1930, Loss: 3.810e-03, Lambda_1: 0.701, Lambda_2: 0.003684, Time: 0.11\n",
      "It: 1940, Loss: 3.764e-03, Lambda_1: 0.703, Lambda_2: 0.003692, Time: 0.11\n",
      "It: 1950, Loss: 3.719e-03, Lambda_1: 0.706, Lambda_2: 0.003700, Time: 0.11\n",
      "It: 1960, Loss: 3.673e-03, Lambda_1: 0.708, Lambda_2: 0.003707, Time: 0.11\n",
      "It: 1970, Loss: 3.628e-03, Lambda_1: 0.710, Lambda_2: 0.003715, Time: 0.11\n",
      "It: 1980, Loss: 3.583e-03, Lambda_1: 0.713, Lambda_2: 0.003723, Time: 0.11\n",
      "It: 1990, Loss: 3.538e-03, Lambda_1: 0.715, Lambda_2: 0.003730, Time: 0.11\n",
      "It: 2000, Loss: 3.493e-03, Lambda_1: 0.717, Lambda_2: 0.003738, Time: 0.12\n",
      "It: 2010, Loss: 3.449e-03, Lambda_1: 0.720, Lambda_2: 0.003746, Time: 0.11\n",
      "It: 2020, Loss: 3.405e-03, Lambda_1: 0.722, Lambda_2: 0.003753, Time: 0.11\n",
      "It: 2030, Loss: 3.361e-03, Lambda_1: 0.724, Lambda_2: 0.003761, Time: 0.11\n",
      "It: 2040, Loss: 3.318e-03, Lambda_1: 0.726, Lambda_2: 0.003769, Time: 0.11\n",
      "It: 2050, Loss: 3.274e-03, Lambda_1: 0.729, Lambda_2: 0.003776, Time: 0.11\n",
      "It: 2060, Loss: 3.231e-03, Lambda_1: 0.731, Lambda_2: 0.003784, Time: 0.11\n",
      "It: 2070, Loss: 3.188e-03, Lambda_1: 0.733, Lambda_2: 0.003792, Time: 0.11\n",
      "It: 2080, Loss: 3.146e-03, Lambda_1: 0.735, Lambda_2: 0.003799, Time: 0.11\n",
      "It: 2090, Loss: 3.104e-03, Lambda_1: 0.738, Lambda_2: 0.003807, Time: 0.11\n",
      "It: 2100, Loss: 3.062e-03, Lambda_1: 0.740, Lambda_2: 0.003815, Time: 0.11\n",
      "It: 2110, Loss: 3.020e-03, Lambda_1: 0.742, Lambda_2: 0.003822, Time: 0.11\n",
      "It: 2120, Loss: 2.987e-03, Lambda_1: 0.744, Lambda_2: 0.003830, Time: 0.11\n",
      "It: 2130, Loss: 2.974e-03, Lambda_1: 0.747, Lambda_2: 0.003836, Time: 0.11\n",
      "It: 2140, Loss: 3.008e-03, Lambda_1: 0.749, Lambda_2: 0.003843, Time: 0.11\n",
      "It: 2150, Loss: 3.082e-03, Lambda_1: 0.751, Lambda_2: 0.003849, Time: 0.11\n",
      "It: 2160, Loss: 2.980e-03, Lambda_1: 0.753, Lambda_2: 0.003855, Time: 0.11\n",
      "It: 2170, Loss: 3.448e-03, Lambda_1: 0.755, Lambda_2: 0.003860, Time: 0.11\n",
      "It: 2180, Loss: 2.918e-03, Lambda_1: 0.757, Lambda_2: 0.003863, Time: 0.11\n",
      "It: 2190, Loss: 2.944e-03, Lambda_1: 0.758, Lambda_2: 0.003869, Time: 0.11\n",
      "It: 2200, Loss: 2.793e-03, Lambda_1: 0.760, Lambda_2: 0.003876, Time: 0.11\n",
      "It: 2210, Loss: 2.717e-03, Lambda_1: 0.761, Lambda_2: 0.003881, Time: 0.11\n",
      "It: 2220, Loss: 2.727e-03, Lambda_1: 0.763, Lambda_2: 0.003886, Time: 0.11\n",
      "It: 2230, Loss: 2.735e-03, Lambda_1: 0.764, Lambda_2: 0.003891, Time: 0.11\n",
      "It: 2240, Loss: 2.904e-03, Lambda_1: 0.766, Lambda_2: 0.003895, Time: 0.11\n",
      "It: 2250, Loss: 2.997e-03, Lambda_1: 0.768, Lambda_2: 0.003899, Time: 0.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 2260, Loss: 2.552e-03, Lambda_1: 0.770, Lambda_2: 0.003903, Time: 0.11\n",
      "It: 2270, Loss: 2.476e-03, Lambda_1: 0.771, Lambda_2: 0.003907, Time: 0.11\n",
      "It: 2280, Loss: 2.432e-03, Lambda_1: 0.773, Lambda_2: 0.003912, Time: 0.11\n",
      "It: 2290, Loss: 2.396e-03, Lambda_1: 0.775, Lambda_2: 0.003916, Time: 0.11\n",
      "It: 2300, Loss: 2.364e-03, Lambda_1: 0.776, Lambda_2: 0.003920, Time: 0.11\n",
      "It: 2310, Loss: 2.333e-03, Lambda_1: 0.778, Lambda_2: 0.003924, Time: 0.11\n",
      "It: 2320, Loss: 2.341e-03, Lambda_1: 0.780, Lambda_2: 0.003928, Time: 0.11\n",
      "It: 2330, Loss: 2.279e-03, Lambda_1: 0.782, Lambda_2: 0.003931, Time: 0.11\n",
      "It: 2340, Loss: 2.421e-03, Lambda_1: 0.784, Lambda_2: 0.003935, Time: 0.11\n",
      "It: 2350, Loss: 2.490e-03, Lambda_1: 0.785, Lambda_2: 0.003938, Time: 0.11\n",
      "It: 2360, Loss: 2.200e-03, Lambda_1: 0.787, Lambda_2: 0.003941, Time: 0.11\n",
      "It: 2370, Loss: 2.463e-03, Lambda_1: 0.789, Lambda_2: 0.003944, Time: 0.11\n",
      "It: 2380, Loss: 3.237e-03, Lambda_1: 0.791, Lambda_2: 0.003946, Time: 0.11\n",
      "It: 2390, Loss: 2.377e-03, Lambda_1: 0.792, Lambda_2: 0.003948, Time: 0.11\n",
      "It: 2400, Loss: 2.193e-03, Lambda_1: 0.793, Lambda_2: 0.003952, Time: 0.11\n",
      "It: 2410, Loss: 2.092e-03, Lambda_1: 0.794, Lambda_2: 0.003956, Time: 0.11\n",
      "It: 2420, Loss: 2.045e-03, Lambda_1: 0.795, Lambda_2: 0.003960, Time: 0.12\n",
      "It: 2430, Loss: 2.007e-03, Lambda_1: 0.797, Lambda_2: 0.003964, Time: 0.11\n",
      "It: 2440, Loss: 1.994e-03, Lambda_1: 0.798, Lambda_2: 0.003967, Time: 0.11\n",
      "It: 2450, Loss: 2.026e-03, Lambda_1: 0.800, Lambda_2: 0.003970, Time: 0.11\n",
      "It: 2460, Loss: 1.971e-03, Lambda_1: 0.802, Lambda_2: 0.003972, Time: 0.11\n",
      "It: 2470, Loss: 1.904e-03, Lambda_1: 0.803, Lambda_2: 0.003975, Time: 0.11\n",
      "It: 2480, Loss: 1.922e-03, Lambda_1: 0.805, Lambda_2: 0.003977, Time: 0.11\n",
      "It: 2490, Loss: 1.874e-03, Lambda_1: 0.807, Lambda_2: 0.003980, Time: 0.11\n",
      "It: 2500, Loss: 2.568e-03, Lambda_1: 0.808, Lambda_2: 0.003983, Time: 0.11\n",
      "It: 2510, Loss: 2.496e-03, Lambda_1: 0.810, Lambda_2: 0.003982, Time: 0.11\n",
      "It: 2520, Loss: 1.997e-03, Lambda_1: 0.811, Lambda_2: 0.003985, Time: 0.11\n",
      "It: 2530, Loss: 2.356e-03, Lambda_1: 0.812, Lambda_2: 0.003988, Time: 0.11\n",
      "It: 2540, Loss: 2.108e-03, Lambda_1: 0.814, Lambda_2: 0.003990, Time: 0.11\n",
      "It: 2550, Loss: 1.819e-03, Lambda_1: 0.815, Lambda_2: 0.003993, Time: 0.11\n",
      "It: 2560, Loss: 1.721e-03, Lambda_1: 0.816, Lambda_2: 0.003995, Time: 0.11\n",
      "It: 2570, Loss: 1.723e-03, Lambda_1: 0.817, Lambda_2: 0.003998, Time: 0.11\n",
      "It: 2580, Loss: 1.694e-03, Lambda_1: 0.818, Lambda_2: 0.004001, Time: 0.11\n",
      "It: 2590, Loss: 1.646e-03, Lambda_1: 0.820, Lambda_2: 0.004003, Time: 0.11\n",
      "It: 2600, Loss: 1.672e-03, Lambda_1: 0.821, Lambda_2: 0.004005, Time: 0.11\n",
      "It: 2610, Loss: 1.608e-03, Lambda_1: 0.822, Lambda_2: 0.004007, Time: 0.11\n",
      "It: 2620, Loss: 1.591e-03, Lambda_1: 0.824, Lambda_2: 0.004009, Time: 0.11\n",
      "It: 2630, Loss: 1.866e-03, Lambda_1: 0.825, Lambda_2: 0.004011, Time: 0.11\n",
      "It: 2640, Loss: 2.455e-03, Lambda_1: 0.827, Lambda_2: 0.004011, Time: 0.11\n",
      "It: 2650, Loss: 1.748e-03, Lambda_1: 0.828, Lambda_2: 0.004012, Time: 0.11\n",
      "It: 2660, Loss: 1.906e-03, Lambda_1: 0.830, Lambda_2: 0.004013, Time: 0.11\n",
      "It: 2670, Loss: 1.659e-03, Lambda_1: 0.831, Lambda_2: 0.004015, Time: 0.11\n",
      "It: 2680, Loss: 1.554e-03, Lambda_1: 0.832, Lambda_2: 0.004018, Time: 0.11\n",
      "It: 2690, Loss: 1.480e-03, Lambda_1: 0.833, Lambda_2: 0.004020, Time: 0.11\n",
      "It: 2700, Loss: 1.591e-03, Lambda_1: 0.834, Lambda_2: 0.004023, Time: 0.11\n",
      "It: 2710, Loss: 1.439e-03, Lambda_1: 0.835, Lambda_2: 0.004024, Time: 0.11\n",
      "It: 2720, Loss: 1.919e-03, Lambda_1: 0.836, Lambda_2: 0.004026, Time: 0.11\n",
      "It: 2730, Loss: 1.395e-01, Lambda_1: 0.837, Lambda_2: 0.004026, Time: 0.11\n",
      "It: 2740, Loss: 1.120e-01, Lambda_1: 0.835, Lambda_2: 0.004006, Time: 0.11\n",
      "It: 2750, Loss: 3.330e-02, Lambda_1: 0.837, Lambda_2: 0.003990, Time: 0.11\n",
      "It: 2760, Loss: 8.946e-03, Lambda_1: 0.836, Lambda_2: 0.003982, Time: 0.11\n",
      "It: 2770, Loss: 8.123e-03, Lambda_1: 0.830, Lambda_2: 0.003994, Time: 0.11\n",
      "It: 2780, Loss: 4.721e-03, Lambda_1: 0.825, Lambda_2: 0.004000, Time: 0.11\n",
      "It: 2790, Loss: 3.218e-03, Lambda_1: 0.821, Lambda_2: 0.004012, Time: 0.11\n",
      "It: 2800, Loss: 2.493e-03, Lambda_1: 0.818, Lambda_2: 0.004018, Time: 0.11\n",
      "It: 2810, Loss: 2.087e-03, Lambda_1: 0.816, Lambda_2: 0.004023, Time: 0.11\n",
      "It: 2820, Loss: 1.913e-03, Lambda_1: 0.815, Lambda_2: 0.004027, Time: 0.11\n",
      "It: 2830, Loss: 1.785e-03, Lambda_1: 0.814, Lambda_2: 0.004030, Time: 0.11\n",
      "It: 2840, Loss: 1.710e-03, Lambda_1: 0.814, Lambda_2: 0.004033, Time: 0.11\n",
      "It: 2850, Loss: 1.657e-03, Lambda_1: 0.814, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 2860, Loss: 1.619e-03, Lambda_1: 0.814, Lambda_2: 0.004037, Time: 0.11\n",
      "It: 2870, Loss: 1.592e-03, Lambda_1: 0.815, Lambda_2: 0.004039, Time: 0.11\n",
      "It: 2880, Loss: 1.571e-03, Lambda_1: 0.815, Lambda_2: 0.004040, Time: 0.11\n",
      "It: 2890, Loss: 1.554e-03, Lambda_1: 0.816, Lambda_2: 0.004041, Time: 0.11\n",
      "It: 2900, Loss: 1.539e-03, Lambda_1: 0.817, Lambda_2: 0.004042, Time: 0.11\n",
      "It: 2910, Loss: 1.525e-03, Lambda_1: 0.817, Lambda_2: 0.004043, Time: 0.11\n",
      "It: 2920, Loss: 1.512e-03, Lambda_1: 0.818, Lambda_2: 0.004044, Time: 0.11\n",
      "It: 2930, Loss: 1.500e-03, Lambda_1: 0.819, Lambda_2: 0.004045, Time: 0.11\n",
      "It: 2940, Loss: 1.489e-03, Lambda_1: 0.820, Lambda_2: 0.004046, Time: 0.11\n",
      "It: 2950, Loss: 1.478e-03, Lambda_1: 0.820, Lambda_2: 0.004046, Time: 0.11\n",
      "It: 2960, Loss: 1.467e-03, Lambda_1: 0.821, Lambda_2: 0.004047, Time: 0.11\n",
      "It: 2970, Loss: 1.457e-03, Lambda_1: 0.822, Lambda_2: 0.004048, Time: 0.11\n",
      "It: 2980, Loss: 1.446e-03, Lambda_1: 0.823, Lambda_2: 0.004048, Time: 0.11\n",
      "It: 2990, Loss: 1.436e-03, Lambda_1: 0.824, Lambda_2: 0.004049, Time: 0.11\n",
      "It: 3000, Loss: 1.426e-03, Lambda_1: 0.825, Lambda_2: 0.004050, Time: 0.11\n",
      "It: 3010, Loss: 1.417e-03, Lambda_1: 0.826, Lambda_2: 0.004050, Time: 0.11\n",
      "It: 3020, Loss: 1.407e-03, Lambda_1: 0.827, Lambda_2: 0.004051, Time: 0.11\n",
      "It: 3030, Loss: 1.398e-03, Lambda_1: 0.827, Lambda_2: 0.004052, Time: 0.11\n",
      "It: 3040, Loss: 1.388e-03, Lambda_1: 0.828, Lambda_2: 0.004052, Time: 0.11\n",
      "It: 3050, Loss: 1.379e-03, Lambda_1: 0.829, Lambda_2: 0.004053, Time: 0.11\n",
      "It: 3060, Loss: 1.370e-03, Lambda_1: 0.830, Lambda_2: 0.004054, Time: 0.11\n",
      "It: 3070, Loss: 1.361e-03, Lambda_1: 0.831, Lambda_2: 0.004054, Time: 0.11\n",
      "It: 3080, Loss: 1.352e-03, Lambda_1: 0.832, Lambda_2: 0.004055, Time: 0.11\n",
      "It: 3090, Loss: 1.343e-03, Lambda_1: 0.833, Lambda_2: 0.004056, Time: 0.11\n",
      "It: 3100, Loss: 1.334e-03, Lambda_1: 0.834, Lambda_2: 0.004056, Time: 0.11\n",
      "It: 3110, Loss: 1.325e-03, Lambda_1: 0.834, Lambda_2: 0.004057, Time: 0.11\n",
      "It: 3120, Loss: 1.317e-03, Lambda_1: 0.835, Lambda_2: 0.004057, Time: 0.11\n",
      "It: 3130, Loss: 1.308e-03, Lambda_1: 0.836, Lambda_2: 0.004058, Time: 0.11\n",
      "It: 3140, Loss: 1.300e-03, Lambda_1: 0.837, Lambda_2: 0.004059, Time: 0.11\n",
      "It: 3150, Loss: 1.291e-03, Lambda_1: 0.838, Lambda_2: 0.004059, Time: 0.11\n",
      "It: 3160, Loss: 1.283e-03, Lambda_1: 0.839, Lambda_2: 0.004060, Time: 0.11\n",
      "It: 3170, Loss: 1.275e-03, Lambda_1: 0.840, Lambda_2: 0.004061, Time: 0.11\n",
      "It: 3180, Loss: 1.267e-03, Lambda_1: 0.840, Lambda_2: 0.004061, Time: 0.11\n",
      "It: 3190, Loss: 1.259e-03, Lambda_1: 0.841, Lambda_2: 0.004062, Time: 0.11\n",
      "It: 3200, Loss: 1.250e-03, Lambda_1: 0.842, Lambda_2: 0.004063, Time: 0.11\n",
      "It: 3210, Loss: 1.242e-03, Lambda_1: 0.843, Lambda_2: 0.004063, Time: 0.11\n",
      "It: 3220, Loss: 1.234e-03, Lambda_1: 0.844, Lambda_2: 0.004064, Time: 0.11\n",
      "It: 3230, Loss: 1.227e-03, Lambda_1: 0.845, Lambda_2: 0.004064, Time: 0.11\n",
      "It: 3240, Loss: 1.219e-03, Lambda_1: 0.846, Lambda_2: 0.004065, Time: 0.11\n",
      "It: 3250, Loss: 1.211e-03, Lambda_1: 0.846, Lambda_2: 0.004066, Time: 0.11\n",
      "It: 3260, Loss: 1.203e-03, Lambda_1: 0.847, Lambda_2: 0.004066, Time: 0.11\n",
      "It: 3270, Loss: 1.196e-03, Lambda_1: 0.848, Lambda_2: 0.004067, Time: 0.11\n",
      "It: 3280, Loss: 1.188e-03, Lambda_1: 0.849, Lambda_2: 0.004068, Time: 0.11\n",
      "It: 3290, Loss: 1.180e-03, Lambda_1: 0.850, Lambda_2: 0.004068, Time: 0.11\n",
      "It: 3300, Loss: 1.173e-03, Lambda_1: 0.851, Lambda_2: 0.004069, Time: 0.11\n",
      "It: 3310, Loss: 1.165e-03, Lambda_1: 0.851, Lambda_2: 0.004070, Time: 0.11\n",
      "It: 3320, Loss: 1.158e-03, Lambda_1: 0.852, Lambda_2: 0.004070, Time: 0.12\n",
      "It: 3330, Loss: 1.151e-03, Lambda_1: 0.853, Lambda_2: 0.004071, Time: 0.12\n",
      "It: 3340, Loss: 1.143e-03, Lambda_1: 0.854, Lambda_2: 0.004071, Time: 0.11\n",
      "It: 3350, Loss: 1.136e-03, Lambda_1: 0.855, Lambda_2: 0.004072, Time: 0.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 3360, Loss: 1.129e-03, Lambda_1: 0.855, Lambda_2: 0.004073, Time: 0.11\n",
      "It: 3370, Loss: 1.122e-03, Lambda_1: 0.856, Lambda_2: 0.004073, Time: 0.11\n",
      "It: 3380, Loss: 1.115e-03, Lambda_1: 0.857, Lambda_2: 0.004074, Time: 0.11\n",
      "It: 3390, Loss: 1.107e-03, Lambda_1: 0.858, Lambda_2: 0.004075, Time: 0.11\n",
      "It: 3400, Loss: 1.100e-03, Lambda_1: 0.859, Lambda_2: 0.004075, Time: 0.11\n",
      "It: 3410, Loss: 1.093e-03, Lambda_1: 0.859, Lambda_2: 0.004076, Time: 0.13\n",
      "It: 3420, Loss: 1.086e-03, Lambda_1: 0.860, Lambda_2: 0.004077, Time: 0.12\n",
      "It: 3430, Loss: 1.080e-03, Lambda_1: 0.861, Lambda_2: 0.004077, Time: 0.11\n",
      "It: 3440, Loss: 1.073e-03, Lambda_1: 0.862, Lambda_2: 0.004078, Time: 0.11\n",
      "It: 3450, Loss: 1.066e-03, Lambda_1: 0.863, Lambda_2: 0.004079, Time: 0.11\n",
      "It: 3460, Loss: 1.059e-03, Lambda_1: 0.863, Lambda_2: 0.004079, Time: 0.13\n",
      "It: 3470, Loss: 1.052e-03, Lambda_1: 0.864, Lambda_2: 0.004080, Time: 0.13\n",
      "It: 3480, Loss: 1.046e-03, Lambda_1: 0.865, Lambda_2: 0.004080, Time: 0.11\n",
      "It: 3490, Loss: 1.039e-03, Lambda_1: 0.866, Lambda_2: 0.004081, Time: 0.11\n",
      "It: 3500, Loss: 1.032e-03, Lambda_1: 0.867, Lambda_2: 0.004082, Time: 0.11\n",
      "It: 3510, Loss: 1.026e-03, Lambda_1: 0.867, Lambda_2: 0.004082, Time: 0.13\n",
      "It: 3520, Loss: 1.019e-03, Lambda_1: 0.868, Lambda_2: 0.004083, Time: 0.12\n",
      "It: 3530, Loss: 1.013e-03, Lambda_1: 0.869, Lambda_2: 0.004084, Time: 0.11\n",
      "It: 3540, Loss: 1.006e-03, Lambda_1: 0.870, Lambda_2: 0.004084, Time: 0.11\n",
      "It: 3550, Loss: 9.996e-04, Lambda_1: 0.870, Lambda_2: 0.004085, Time: 0.11\n",
      "It: 3560, Loss: 9.932e-04, Lambda_1: 0.871, Lambda_2: 0.004085, Time: 0.11\n",
      "It: 3570, Loss: 9.868e-04, Lambda_1: 0.872, Lambda_2: 0.004086, Time: 0.11\n",
      "It: 3580, Loss: 9.805e-04, Lambda_1: 0.873, Lambda_2: 0.004087, Time: 0.11\n",
      "It: 3590, Loss: 9.741e-04, Lambda_1: 0.873, Lambda_2: 0.004087, Time: 0.11\n",
      "It: 3600, Loss: 9.679e-04, Lambda_1: 0.874, Lambda_2: 0.004088, Time: 0.11\n",
      "It: 3610, Loss: 9.616e-04, Lambda_1: 0.875, Lambda_2: 0.004089, Time: 0.11\n",
      "It: 3620, Loss: 9.554e-04, Lambda_1: 0.876, Lambda_2: 0.004089, Time: 0.12\n",
      "It: 3630, Loss: 9.492e-04, Lambda_1: 0.876, Lambda_2: 0.004090, Time: 0.12\n",
      "It: 3640, Loss: 9.431e-04, Lambda_1: 0.877, Lambda_2: 0.004090, Time: 0.11\n",
      "It: 3650, Loss: 9.370e-04, Lambda_1: 0.878, Lambda_2: 0.004091, Time: 0.11\n",
      "It: 3660, Loss: 9.309e-04, Lambda_1: 0.878, Lambda_2: 0.004092, Time: 0.11\n",
      "It: 3670, Loss: 9.248e-04, Lambda_1: 0.879, Lambda_2: 0.004092, Time: 0.11\n",
      "It: 3680, Loss: 9.188e-04, Lambda_1: 0.880, Lambda_2: 0.004093, Time: 0.11\n",
      "It: 3690, Loss: 9.128e-04, Lambda_1: 0.881, Lambda_2: 0.004093, Time: 0.12\n",
      "It: 3700, Loss: 9.068e-04, Lambda_1: 0.881, Lambda_2: 0.004094, Time: 0.11\n",
      "It: 3710, Loss: 9.009e-04, Lambda_1: 0.882, Lambda_2: 0.004095, Time: 0.11\n",
      "It: 3720, Loss: 8.950e-04, Lambda_1: 0.883, Lambda_2: 0.004095, Time: 0.11\n",
      "It: 3730, Loss: 8.891e-04, Lambda_1: 0.884, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 3740, Loss: 8.833e-04, Lambda_1: 0.884, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 3750, Loss: 8.775e-04, Lambda_1: 0.885, Lambda_2: 0.004097, Time: 0.11\n",
      "It: 3760, Loss: 8.717e-04, Lambda_1: 0.886, Lambda_2: 0.004097, Time: 0.11\n",
      "It: 3770, Loss: 8.660e-04, Lambda_1: 0.886, Lambda_2: 0.004098, Time: 0.11\n",
      "It: 3780, Loss: 8.602e-04, Lambda_1: 0.887, Lambda_2: 0.004099, Time: 0.12\n",
      "It: 3790, Loss: 8.546e-04, Lambda_1: 0.888, Lambda_2: 0.004099, Time: 0.11\n",
      "It: 3800, Loss: 8.489e-04, Lambda_1: 0.888, Lambda_2: 0.004100, Time: 0.13\n",
      "It: 3810, Loss: 8.433e-04, Lambda_1: 0.889, Lambda_2: 0.004100, Time: 0.13\n",
      "It: 3820, Loss: 8.377e-04, Lambda_1: 0.890, Lambda_2: 0.004101, Time: 0.12\n",
      "It: 3830, Loss: 8.321e-04, Lambda_1: 0.890, Lambda_2: 0.004101, Time: 0.11\n",
      "It: 3840, Loss: 8.265e-04, Lambda_1: 0.891, Lambda_2: 0.004102, Time: 0.11\n",
      "It: 3850, Loss: 8.210e-04, Lambda_1: 0.892, Lambda_2: 0.004103, Time: 0.12\n",
      "It: 3860, Loss: 8.155e-04, Lambda_1: 0.892, Lambda_2: 0.004103, Time: 0.11\n",
      "It: 3870, Loss: 8.100e-04, Lambda_1: 0.893, Lambda_2: 0.004104, Time: 0.12\n",
      "It: 3880, Loss: 8.046e-04, Lambda_1: 0.894, Lambda_2: 0.004104, Time: 0.13\n",
      "It: 3890, Loss: 7.992e-04, Lambda_1: 0.894, Lambda_2: 0.004105, Time: 0.13\n",
      "It: 3900, Loss: 7.938e-04, Lambda_1: 0.895, Lambda_2: 0.004105, Time: 0.12\n",
      "It: 3910, Loss: 7.885e-04, Lambda_1: 0.896, Lambda_2: 0.004106, Time: 0.12\n",
      "It: 3920, Loss: 7.831e-04, Lambda_1: 0.896, Lambda_2: 0.004106, Time: 0.13\n",
      "It: 3930, Loss: 7.778e-04, Lambda_1: 0.897, Lambda_2: 0.004107, Time: 0.12\n",
      "It: 3940, Loss: 7.726e-04, Lambda_1: 0.898, Lambda_2: 0.004107, Time: 0.12\n",
      "It: 3950, Loss: 7.673e-04, Lambda_1: 0.898, Lambda_2: 0.004108, Time: 0.12\n",
      "It: 3960, Loss: 7.621e-04, Lambda_1: 0.899, Lambda_2: 0.004108, Time: 0.11\n",
      "It: 3970, Loss: 7.569e-04, Lambda_1: 0.900, Lambda_2: 0.004109, Time: 0.12\n",
      "It: 3980, Loss: 7.517e-04, Lambda_1: 0.900, Lambda_2: 0.004109, Time: 0.12\n",
      "It: 3990, Loss: 7.466e-04, Lambda_1: 0.901, Lambda_2: 0.004110, Time: 0.12\n",
      "It: 4000, Loss: 7.415e-04, Lambda_1: 0.902, Lambda_2: 0.004110, Time: 0.12\n",
      "It: 4010, Loss: 7.364e-04, Lambda_1: 0.902, Lambda_2: 0.004111, Time: 0.12\n",
      "It: 4020, Loss: 7.313e-04, Lambda_1: 0.903, Lambda_2: 0.004111, Time: 0.12\n",
      "It: 4030, Loss: 7.263e-04, Lambda_1: 0.904, Lambda_2: 0.004112, Time: 0.12\n",
      "It: 4040, Loss: 7.213e-04, Lambda_1: 0.904, Lambda_2: 0.004112, Time: 0.13\n",
      "It: 4050, Loss: 7.163e-04, Lambda_1: 0.905, Lambda_2: 0.004113, Time: 0.12\n",
      "It: 4060, Loss: 7.114e-04, Lambda_1: 0.905, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 4070, Loss: 7.065e-04, Lambda_1: 0.906, Lambda_2: 0.004114, Time: 0.11\n",
      "It: 4080, Loss: 7.016e-04, Lambda_1: 0.907, Lambda_2: 0.004114, Time: 0.12\n",
      "It: 4090, Loss: 6.967e-04, Lambda_1: 0.907, Lambda_2: 0.004115, Time: 0.12\n",
      "It: 4100, Loss: 6.918e-04, Lambda_1: 0.908, Lambda_2: 0.004115, Time: 0.12\n",
      "It: 4110, Loss: 6.870e-04, Lambda_1: 0.909, Lambda_2: 0.004116, Time: 0.11\n",
      "It: 4120, Loss: 6.822e-04, Lambda_1: 0.909, Lambda_2: 0.004116, Time: 0.11\n",
      "It: 4130, Loss: 6.775e-04, Lambda_1: 0.910, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 4140, Loss: 6.728e-04, Lambda_1: 0.910, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 4150, Loss: 6.680e-04, Lambda_1: 0.911, Lambda_2: 0.004117, Time: 0.12\n",
      "It: 4160, Loss: 6.634e-04, Lambda_1: 0.912, Lambda_2: 0.004118, Time: 0.13\n",
      "It: 4170, Loss: 6.587e-04, Lambda_1: 0.912, Lambda_2: 0.004118, Time: 0.12\n",
      "It: 4180, Loss: 6.541e-04, Lambda_1: 0.913, Lambda_2: 0.004119, Time: 0.12\n",
      "It: 4190, Loss: 6.495e-04, Lambda_1: 0.913, Lambda_2: 0.004119, Time: 0.12\n",
      "It: 4200, Loss: 6.449e-04, Lambda_1: 0.914, Lambda_2: 0.004120, Time: 0.13\n",
      "It: 4210, Loss: 6.404e-04, Lambda_1: 0.915, Lambda_2: 0.004120, Time: 0.13\n",
      "It: 4220, Loss: 6.359e-04, Lambda_1: 0.915, Lambda_2: 0.004120, Time: 0.11\n",
      "It: 4230, Loss: 6.314e-04, Lambda_1: 0.916, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 4240, Loss: 6.269e-04, Lambda_1: 0.916, Lambda_2: 0.004121, Time: 0.13\n",
      "It: 4250, Loss: 6.225e-04, Lambda_1: 0.917, Lambda_2: 0.004122, Time: 0.12\n",
      "It: 4260, Loss: 6.181e-04, Lambda_1: 0.918, Lambda_2: 0.004122, Time: 0.11\n",
      "It: 4270, Loss: 6.137e-04, Lambda_1: 0.918, Lambda_2: 0.004122, Time: 0.11\n",
      "It: 4280, Loss: 6.094e-04, Lambda_1: 0.919, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 4290, Loss: 6.051e-04, Lambda_1: 0.919, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 4300, Loss: 6.008e-04, Lambda_1: 0.920, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 4310, Loss: 5.965e-04, Lambda_1: 0.920, Lambda_2: 0.004124, Time: 0.11\n",
      "It: 4320, Loss: 5.923e-04, Lambda_1: 0.921, Lambda_2: 0.004124, Time: 0.12\n",
      "It: 4330, Loss: 5.881e-04, Lambda_1: 0.922, Lambda_2: 0.004124, Time: 0.12\n",
      "It: 4340, Loss: 5.839e-04, Lambda_1: 0.922, Lambda_2: 0.004125, Time: 0.12\n",
      "It: 4350, Loss: 5.798e-04, Lambda_1: 0.923, Lambda_2: 0.004125, Time: 0.11\n",
      "It: 4360, Loss: 5.757e-04, Lambda_1: 0.923, Lambda_2: 0.004125, Time: 0.11\n",
      "It: 4370, Loss: 5.716e-04, Lambda_1: 0.924, Lambda_2: 0.004126, Time: 0.11\n",
      "It: 4380, Loss: 5.675e-04, Lambda_1: 0.924, Lambda_2: 0.004126, Time: 0.11\n",
      "It: 4390, Loss: 5.635e-04, Lambda_1: 0.925, Lambda_2: 0.004126, Time: 0.11\n",
      "It: 4400, Loss: 5.595e-04, Lambda_1: 0.925, Lambda_2: 0.004127, Time: 0.12\n",
      "It: 4410, Loss: 5.556e-04, Lambda_1: 0.926, Lambda_2: 0.004127, Time: 0.12\n",
      "It: 4420, Loss: 5.516e-04, Lambda_1: 0.927, Lambda_2: 0.004127, Time: 0.12\n",
      "It: 4430, Loss: 5.477e-04, Lambda_1: 0.927, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 4440, Loss: 5.438e-04, Lambda_1: 0.928, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 4450, Loss: 5.400e-04, Lambda_1: 0.928, Lambda_2: 0.004128, Time: 0.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 4460, Loss: 5.404e-04, Lambda_1: 0.929, Lambda_2: 0.004128, Time: 0.12\n",
      "It: 4470, Loss: 7.643e-04, Lambda_1: 0.929, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 4480, Loss: 9.973e-04, Lambda_1: 0.930, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 4490, Loss: 6.640e-04, Lambda_1: 0.930, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 4500, Loss: 6.689e-04, Lambda_1: 0.931, Lambda_2: 0.004129, Time: 0.12\n",
      "It: 4510, Loss: 5.549e-04, Lambda_1: 0.931, Lambda_2: 0.004129, Time: 0.12\n",
      "It: 4520, Loss: 5.492e-04, Lambda_1: 0.931, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 4530, Loss: 5.942e-04, Lambda_1: 0.932, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 4540, Loss: 6.388e-04, Lambda_1: 0.932, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 4550, Loss: 6.551e-04, Lambda_1: 0.933, Lambda_2: 0.004130, Time: 0.12\n",
      "It: 4560, Loss: 7.224e-04, Lambda_1: 0.933, Lambda_2: 0.004130, Time: 0.12\n",
      "It: 4570, Loss: 5.554e-04, Lambda_1: 0.934, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 4580, Loss: 5.136e-04, Lambda_1: 0.934, Lambda_2: 0.004130, Time: 0.12\n",
      "It: 4590, Loss: 5.817e-04, Lambda_1: 0.934, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 4600, Loss: 6.894e-04, Lambda_1: 0.935, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 4610, Loss: 6.499e-04, Lambda_1: 0.935, Lambda_2: 0.004130, Time: 0.13\n",
      "It: 4620, Loss: 5.412e-04, Lambda_1: 0.935, Lambda_2: 0.004131, Time: 0.13\n",
      "It: 4630, Loss: 5.017e-04, Lambda_1: 0.936, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 4640, Loss: 6.079e-04, Lambda_1: 0.936, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 4650, Loss: 6.749e-04, Lambda_1: 0.937, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 4660, Loss: 6.390e-04, Lambda_1: 0.937, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 4670, Loss: 4.817e-04, Lambda_1: 0.937, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 4680, Loss: 5.319e-04, Lambda_1: 0.938, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4690, Loss: 6.368e-04, Lambda_1: 0.938, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 4700, Loss: 6.742e-04, Lambda_1: 0.938, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4710, Loss: 5.001e-04, Lambda_1: 0.939, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 4720, Loss: 4.792e-04, Lambda_1: 0.939, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4730, Loss: 6.318e-04, Lambda_1: 0.939, Lambda_2: 0.004132, Time: 0.14\n",
      "It: 4740, Loss: 6.426e-04, Lambda_1: 0.940, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4750, Loss: 5.360e-04, Lambda_1: 0.940, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4760, Loss: 4.607e-04, Lambda_1: 0.940, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4770, Loss: 6.224e-04, Lambda_1: 0.941, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4780, Loss: 6.292e-04, Lambda_1: 0.941, Lambda_2: 0.004132, Time: 0.14\n",
      "It: 4790, Loss: 5.919e-04, Lambda_1: 0.941, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4800, Loss: 4.792e-04, Lambda_1: 0.942, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4810, Loss: 6.074e-04, Lambda_1: 0.942, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4820, Loss: 6.457e-04, Lambda_1: 0.942, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4830, Loss: 5.468e-04, Lambda_1: 0.943, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4840, Loss: 5.465e-04, Lambda_1: 0.943, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4850, Loss: 5.791e-04, Lambda_1: 0.943, Lambda_2: 0.004132, Time: 0.13\n",
      "It: 4860, Loss: 7.524e-04, Lambda_1: 0.943, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4870, Loss: 4.526e-04, Lambda_1: 0.944, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4880, Loss: 5.825e-04, Lambda_1: 0.944, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4890, Loss: 6.368e-04, Lambda_1: 0.944, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4900, Loss: 5.346e-04, Lambda_1: 0.945, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4910, Loss: 6.109e-04, Lambda_1: 0.945, Lambda_2: 0.004132, Time: 0.13\n",
      "It: 4920, Loss: 5.165e-04, Lambda_1: 0.945, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4930, Loss: 4.451e-04, Lambda_1: 0.946, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4940, Loss: 6.871e-04, Lambda_1: 0.946, Lambda_2: 0.004132, Time: 0.14\n",
      "It: 4950, Loss: 4.374e-04, Lambda_1: 0.946, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 4960, Loss: 4.108e-04, Lambda_1: 0.946, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4970, Loss: 6.598e-04, Lambda_1: 0.947, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4980, Loss: 4.519e-04, Lambda_1: 0.947, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 4990, Loss: 4.814e-04, Lambda_1: 0.947, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 5000, Loss: 6.111e-04, Lambda_1: 0.947, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 5010, Loss: 5.521e-04, Lambda_1: 0.948, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 5020, Loss: 5.212e-04, Lambda_1: 0.948, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 5030, Loss: 5.725e-04, Lambda_1: 0.948, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 5040, Loss: 6.242e-04, Lambda_1: 0.948, Lambda_2: 0.004132, Time: 0.11\n",
      "It: 5050, Loss: 6.143e-04, Lambda_1: 0.949, Lambda_2: 0.004132, Time: 0.12\n",
      "It: 5060, Loss: 4.503e-04, Lambda_1: 0.949, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 5070, Loss: 3.894e-04, Lambda_1: 0.949, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 5080, Loss: 7.045e-04, Lambda_1: 0.949, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 5090, Loss: 4.172e-04, Lambda_1: 0.950, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 5100, Loss: 3.835e-04, Lambda_1: 0.950, Lambda_2: 0.004131, Time: 0.13\n",
      "It: 5110, Loss: 4.819e-04, Lambda_1: 0.950, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 5120, Loss: 7.069e-04, Lambda_1: 0.950, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 5130, Loss: 4.387e-04, Lambda_1: 0.951, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 5140, Loss: 5.483e-04, Lambda_1: 0.951, Lambda_2: 0.004131, Time: 0.12\n",
      "It: 5150, Loss: 7.698e-04, Lambda_1: 0.951, Lambda_2: 0.004131, Time: 0.13\n",
      "It: 5160, Loss: 5.552e-04, Lambda_1: 0.951, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 5170, Loss: 4.682e-04, Lambda_1: 0.951, Lambda_2: 0.004131, Time: 0.11\n",
      "It: 5180, Loss: 3.705e-04, Lambda_1: 0.952, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 5190, Loss: 6.880e-04, Lambda_1: 0.952, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 5200, Loss: 3.961e-04, Lambda_1: 0.952, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 5210, Loss: 3.614e-04, Lambda_1: 0.952, Lambda_2: 0.004130, Time: 0.13\n",
      "It: 5220, Loss: 4.556e-04, Lambda_1: 0.953, Lambda_2: 0.004130, Time: 0.12\n",
      "It: 5230, Loss: 7.492e-04, Lambda_1: 0.953, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 5240, Loss: 4.251e-04, Lambda_1: 0.953, Lambda_2: 0.004130, Time: 0.11\n",
      "It: 5250, Loss: 5.141e-04, Lambda_1: 0.953, Lambda_2: 0.004130, Time: 0.13\n",
      "It: 5260, Loss: 6.946e-04, Lambda_1: 0.953, Lambda_2: 0.004130, Time: 0.13\n",
      "It: 5270, Loss: 5.493e-04, Lambda_1: 0.954, Lambda_2: 0.004130, Time: 0.12\n",
      "It: 5280, Loss: 4.107e-04, Lambda_1: 0.954, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5290, Loss: 4.182e-04, Lambda_1: 0.954, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5300, Loss: 6.553e-04, Lambda_1: 0.954, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5310, Loss: 4.061e-04, Lambda_1: 0.954, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5320, Loss: 4.444e-04, Lambda_1: 0.955, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5330, Loss: 4.990e-04, Lambda_1: 0.955, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5340, Loss: 6.714e-04, Lambda_1: 0.955, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5350, Loss: 4.556e-04, Lambda_1: 0.955, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 5360, Loss: 5.013e-04, Lambda_1: 0.955, Lambda_2: 0.004129, Time: 0.11\n",
      "It: 5370, Loss: 4.683e-04, Lambda_1: 0.956, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 5380, Loss: 3.470e-04, Lambda_1: 0.956, Lambda_2: 0.004128, Time: 0.12\n",
      "It: 5390, Loss: 7.477e-04, Lambda_1: 0.956, Lambda_2: 0.004128, Time: 0.13\n",
      "It: 5400, Loss: 3.460e-04, Lambda_1: 0.956, Lambda_2: 0.004128, Time: 0.12\n",
      "It: 5410, Loss: 3.553e-04, Lambda_1: 0.956, Lambda_2: 0.004128, Time: 0.11\n",
      "It: 5420, Loss: 4.710e-04, Lambda_1: 0.956, Lambda_2: 0.004128, Time: 0.12\n",
      "It: 5430, Loss: 7.841e-04, Lambda_1: 0.957, Lambda_2: 0.004127, Time: 0.13\n",
      "It: 5440, Loss: 5.078e-04, Lambda_1: 0.957, Lambda_2: 0.004127, Time: 0.11\n",
      "It: 5450, Loss: 4.439e-04, Lambda_1: 0.957, Lambda_2: 0.004127, Time: 0.11\n",
      "It: 5460, Loss: 3.943e-04, Lambda_1: 0.957, Lambda_2: 0.004127, Time: 0.11\n",
      "It: 5470, Loss: 3.851e-04, Lambda_1: 0.957, Lambda_2: 0.004127, Time: 0.11\n",
      "It: 5480, Loss: 7.246e-04, Lambda_1: 0.958, Lambda_2: 0.004127, Time: 0.12\n",
      "It: 5490, Loss: 3.623e-04, Lambda_1: 0.958, Lambda_2: 0.004127, Time: 0.13\n",
      "It: 5500, Loss: 4.164e-04, Lambda_1: 0.958, Lambda_2: 0.004127, Time: 0.12\n",
      "It: 5510, Loss: 6.032e-04, Lambda_1: 0.958, Lambda_2: 0.004127, Time: 0.11\n",
      "It: 5520, Loss: 3.587e-04, Lambda_1: 0.958, Lambda_2: 0.004126, Time: 0.12\n",
      "It: 5530, Loss: 6.801e-04, Lambda_1: 0.959, Lambda_2: 0.004126, Time: 0.13\n",
      "It: 5540, Loss: 3.187e-04, Lambda_1: 0.959, Lambda_2: 0.004126, Time: 0.12\n",
      "It: 5550, Loss: 3.381e-04, Lambda_1: 0.959, Lambda_2: 0.004126, Time: 0.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 5560, Loss: 6.094e-04, Lambda_1: 0.959, Lambda_2: 0.004126, Time: 0.11\n",
      "It: 5570, Loss: 4.096e-04, Lambda_1: 0.959, Lambda_2: 0.004125, Time: 0.11\n",
      "It: 5580, Loss: 5.448e-04, Lambda_1: 0.959, Lambda_2: 0.004126, Time: 0.12\n",
      "It: 5590, Loss: 3.301e-04, Lambda_1: 0.959, Lambda_2: 0.004125, Time: 0.13\n",
      "It: 5600, Loss: 3.048e-04, Lambda_1: 0.960, Lambda_2: 0.004125, Time: 0.12\n",
      "It: 5610, Loss: 5.647e-04, Lambda_1: 0.960, Lambda_2: 0.004125, Time: 0.11\n",
      "It: 5620, Loss: 4.260e-04, Lambda_1: 0.960, Lambda_2: 0.004125, Time: 0.12\n",
      "It: 5630, Loss: 5.016e-04, Lambda_1: 0.960, Lambda_2: 0.004124, Time: 0.14\n",
      "It: 5640, Loss: 3.643e-04, Lambda_1: 0.960, Lambda_2: 0.004124, Time: 0.12\n",
      "It: 5650, Loss: 3.070e-04, Lambda_1: 0.960, Lambda_2: 0.004124, Time: 0.11\n",
      "It: 5660, Loss: 4.113e-04, Lambda_1: 0.961, Lambda_2: 0.004124, Time: 0.11\n",
      "It: 5670, Loss: 6.386e-04, Lambda_1: 0.961, Lambda_2: 0.004124, Time: 0.11\n",
      "It: 5680, Loss: 4.927e-04, Lambda_1: 0.961, Lambda_2: 0.004124, Time: 0.13\n",
      "It: 5690, Loss: 3.782e-04, Lambda_1: 0.961, Lambda_2: 0.004123, Time: 0.13\n",
      "It: 5700, Loss: 3.011e-04, Lambda_1: 0.961, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 5710, Loss: 8.933e-04, Lambda_1: 0.961, Lambda_2: 0.004124, Time: 0.11\n",
      "It: 5720, Loss: 3.482e-04, Lambda_1: 0.961, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 5730, Loss: 3.940e-04, Lambda_1: 0.962, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 5740, Loss: 3.784e-04, Lambda_1: 0.962, Lambda_2: 0.004123, Time: 0.13\n",
      "It: 5750, Loss: 3.658e-04, Lambda_1: 0.962, Lambda_2: 0.004123, Time: 0.13\n",
      "It: 5760, Loss: 2.823e-04, Lambda_1: 0.962, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 5770, Loss: 3.848e-04, Lambda_1: 0.962, Lambda_2: 0.004123, Time: 0.11\n",
      "It: 5780, Loss: 3.539e-04, Lambda_1: 0.962, Lambda_2: 0.004122, Time: 0.11\n",
      "It: 5790, Loss: 4.497e-04, Lambda_1: 0.963, Lambda_2: 0.004122, Time: 0.11\n",
      "It: 5800, Loss: 9.548e-04, Lambda_1: 0.963, Lambda_2: 0.004122, Time: 0.11\n",
      "It: 5810, Loss: 9.751e-04, Lambda_1: 0.963, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5820, Loss: 3.503e-04, Lambda_1: 0.963, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5830, Loss: 3.112e-04, Lambda_1: 0.963, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5840, Loss: 3.478e-04, Lambda_1: 0.963, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5850, Loss: 3.047e-04, Lambda_1: 0.963, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5860, Loss: 2.996e-04, Lambda_1: 0.963, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5870, Loss: 3.463e-04, Lambda_1: 0.964, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5880, Loss: 2.829e-04, Lambda_1: 0.964, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5890, Loss: 2.719e-04, Lambda_1: 0.964, Lambda_2: 0.004121, Time: 0.11\n",
      "It: 5900, Loss: 4.468e-04, Lambda_1: 0.964, Lambda_2: 0.004120, Time: 0.11\n",
      "It: 5910, Loss: 4.683e-04, Lambda_1: 0.964, Lambda_2: 0.004120, Time: 0.11\n",
      "It: 5920, Loss: 4.135e-04, Lambda_1: 0.965, Lambda_2: 0.004120, Time: 0.11\n",
      "It: 5930, Loss: 9.194e-04, Lambda_1: 0.965, Lambda_2: 0.004119, Time: 0.11\n",
      "It: 5940, Loss: 5.378e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.11\n",
      "It: 5950, Loss: 5.935e-04, Lambda_1: 0.965, Lambda_2: 0.004119, Time: 0.11\n",
      "It: 5960, Loss: 4.826e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.12\n",
      "It: 5970, Loss: 5.245e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.13\n",
      "It: 5980, Loss: 3.550e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.11\n",
      "It: 5990, Loss: 2.874e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.11\n",
      "It: 6000, Loss: 3.649e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.11\n",
      "It: 6010, Loss: 6.390e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.13\n",
      "It: 6020, Loss: 4.663e-04, Lambda_1: 0.965, Lambda_2: 0.004118, Time: 0.13\n",
      "It: 6030, Loss: 3.515e-04, Lambda_1: 0.965, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 6040, Loss: 2.649e-04, Lambda_1: 0.966, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 6050, Loss: 2.583e-04, Lambda_1: 0.966, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 6060, Loss: 2.595e-04, Lambda_1: 0.966, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 6070, Loss: 3.370e-04, Lambda_1: 0.966, Lambda_2: 0.004117, Time: 0.11\n",
      "It: 6080, Loss: 2.809e-04, Lambda_1: 0.966, Lambda_2: 0.004117, Time: 0.12\n",
      "It: 6090, Loss: 2.988e-04, Lambda_1: 0.966, Lambda_2: 0.004117, Time: 0.13\n",
      "It: 6100, Loss: 3.865e-04, Lambda_1: 0.967, Lambda_2: 0.004117, Time: 0.12\n",
      "It: 6110, Loss: 2.536e-04, Lambda_1: 0.967, Lambda_2: 0.004116, Time: 0.11\n",
      "It: 6120, Loss: 5.482e-04, Lambda_1: 0.967, Lambda_2: 0.004116, Time: 0.11\n",
      "It: 6130, Loss: 4.942e-04, Lambda_1: 0.967, Lambda_2: 0.004116, Time: 0.11\n",
      "It: 6140, Loss: 3.073e-04, Lambda_1: 0.967, Lambda_2: 0.004115, Time: 0.11\n",
      "It: 6150, Loss: 1.487e-03, Lambda_1: 0.967, Lambda_2: 0.004116, Time: 0.11\n",
      "It: 6160, Loss: 1.608e-03, Lambda_1: 0.967, Lambda_2: 0.004115, Time: 0.11\n",
      "It: 6170, Loss: 7.490e-04, Lambda_1: 0.967, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6180, Loss: 4.734e-04, Lambda_1: 0.967, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6190, Loss: 1.223e-03, Lambda_1: 0.967, Lambda_2: 0.004114, Time: 0.12\n",
      "It: 6200, Loss: 7.910e-04, Lambda_1: 0.967, Lambda_2: 0.004114, Time: 0.13\n",
      "It: 6210, Loss: 6.142e-04, Lambda_1: 0.967, Lambda_2: 0.004113, Time: 0.13\n",
      "It: 6220, Loss: 2.939e-04, Lambda_1: 0.967, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6230, Loss: 5.114e-04, Lambda_1: 0.966, Lambda_2: 0.004114, Time: 0.13\n",
      "It: 6240, Loss: 3.083e-04, Lambda_1: 0.966, Lambda_2: 0.004114, Time: 0.12\n",
      "It: 6250, Loss: 2.860e-04, Lambda_1: 0.966, Lambda_2: 0.004114, Time: 0.11\n",
      "It: 6260, Loss: 2.927e-04, Lambda_1: 0.967, Lambda_2: 0.004114, Time: 0.12\n",
      "It: 6270, Loss: 2.591e-04, Lambda_1: 0.967, Lambda_2: 0.004114, Time: 0.14\n",
      "It: 6280, Loss: 2.502e-04, Lambda_1: 0.967, Lambda_2: 0.004114, Time: 0.12\n",
      "It: 6290, Loss: 2.468e-04, Lambda_1: 0.967, Lambda_2: 0.004114, Time: 0.11\n",
      "It: 6300, Loss: 2.509e-04, Lambda_1: 0.967, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6310, Loss: 2.396e-04, Lambda_1: 0.967, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6320, Loss: 2.396e-04, Lambda_1: 0.968, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6330, Loss: 2.665e-04, Lambda_1: 0.968, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6340, Loss: 2.593e-04, Lambda_1: 0.968, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6350, Loss: 2.629e-04, Lambda_1: 0.968, Lambda_2: 0.004113, Time: 0.11\n",
      "It: 6360, Loss: 2.449e-04, Lambda_1: 0.968, Lambda_2: 0.004112, Time: 0.11\n",
      "It: 6370, Loss: 2.730e-04, Lambda_1: 0.969, Lambda_2: 0.004112, Time: 0.11\n",
      "It: 6380, Loss: 3.968e-04, Lambda_1: 0.969, Lambda_2: 0.004112, Time: 0.14\n",
      "It: 6390, Loss: 4.092e-04, Lambda_1: 0.969, Lambda_2: 0.004112, Time: 0.12\n",
      "It: 6400, Loss: 4.731e-04, Lambda_1: 0.969, Lambda_2: 0.004111, Time: 0.11\n",
      "It: 6410, Loss: 5.165e-04, Lambda_1: 0.969, Lambda_2: 0.004111, Time: 0.11\n",
      "It: 6420, Loss: 5.290e-04, Lambda_1: 0.969, Lambda_2: 0.004111, Time: 0.13\n",
      "It: 6430, Loss: 2.569e-04, Lambda_1: 0.970, Lambda_2: 0.004110, Time: 0.13\n",
      "It: 6440, Loss: 1.203e-03, Lambda_1: 0.970, Lambda_2: 0.004110, Time: 0.12\n",
      "It: 6450, Loss: 5.720e-04, Lambda_1: 0.970, Lambda_2: 0.004110, Time: 0.11\n",
      "It: 6460, Loss: 2.679e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.11\n",
      "It: 6470, Loss: 5.858e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.11\n",
      "It: 6480, Loss: 3.726e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.13\n",
      "It: 6490, Loss: 3.412e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.13\n",
      "It: 6500, Loss: 3.381e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.11\n",
      "It: 6510, Loss: 2.596e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.11\n",
      "It: 6520, Loss: 4.345e-04, Lambda_1: 0.970, Lambda_2: 0.004109, Time: 0.11\n",
      "It: 6530, Loss: 3.912e-04, Lambda_1: 0.970, Lambda_2: 0.004108, Time: 0.11\n",
      "It: 6540, Loss: 4.612e-04, Lambda_1: 0.970, Lambda_2: 0.004108, Time: 0.11\n",
      "It: 6550, Loss: 3.113e-04, Lambda_1: 0.970, Lambda_2: 0.004108, Time: 0.12\n",
      "It: 6560, Loss: 3.033e-04, Lambda_1: 0.970, Lambda_2: 0.004108, Time: 0.13\n",
      "It: 6570, Loss: 2.389e-04, Lambda_1: 0.971, Lambda_2: 0.004108, Time: 0.12\n",
      "It: 6580, Loss: 8.595e-04, Lambda_1: 0.971, Lambda_2: 0.004108, Time: 0.11\n",
      "It: 6590, Loss: 3.999e-04, Lambda_1: 0.971, Lambda_2: 0.004107, Time: 0.11\n",
      "It: 6600, Loss: 3.792e-04, Lambda_1: 0.971, Lambda_2: 0.004107, Time: 0.11\n",
      "It: 6610, Loss: 2.948e-04, Lambda_1: 0.971, Lambda_2: 0.004107, Time: 0.11\n",
      "It: 6620, Loss: 2.772e-04, Lambda_1: 0.971, Lambda_2: 0.004107, Time: 0.11\n",
      "It: 6630, Loss: 6.897e-04, Lambda_1: 0.971, Lambda_2: 0.004106, Time: 0.11\n",
      "It: 6640, Loss: 3.638e-04, Lambda_1: 0.971, Lambda_2: 0.004106, Time: 0.11\n",
      "It: 6650, Loss: 4.067e-04, Lambda_1: 0.971, Lambda_2: 0.004106, Time: 0.11\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 6660, Loss: 2.528e-04, Lambda_1: 0.971, Lambda_2: 0.004105, Time: 0.11\n",
      "It: 6670, Loss: 4.779e-04, Lambda_1: 0.971, Lambda_2: 0.004105, Time: 0.11\n",
      "It: 6680, Loss: 3.537e-04, Lambda_1: 0.972, Lambda_2: 0.004105, Time: 0.11\n",
      "It: 6690, Loss: 4.376e-04, Lambda_1: 0.972, Lambda_2: 0.004105, Time: 0.11\n",
      "It: 6700, Loss: 3.756e-04, Lambda_1: 0.972, Lambda_2: 0.004105, Time: 0.12\n",
      "It: 6710, Loss: 2.471e-04, Lambda_1: 0.972, Lambda_2: 0.004104, Time: 0.13\n",
      "It: 6720, Loss: 5.833e-04, Lambda_1: 0.972, Lambda_2: 0.004105, Time: 0.14\n",
      "It: 6730, Loss: 2.443e-04, Lambda_1: 0.972, Lambda_2: 0.004104, Time: 0.14\n",
      "It: 6740, Loss: 5.133e-04, Lambda_1: 0.972, Lambda_2: 0.004104, Time: 0.12\n",
      "It: 6750, Loss: 2.417e-04, Lambda_1: 0.972, Lambda_2: 0.004103, Time: 0.12\n",
      "It: 6760, Loss: 3.796e-04, Lambda_1: 0.972, Lambda_2: 0.004104, Time: 0.11\n",
      "It: 6770, Loss: 4.087e-04, Lambda_1: 0.972, Lambda_2: 0.004103, Time: 0.11\n",
      "It: 6780, Loss: 3.229e-04, Lambda_1: 0.972, Lambda_2: 0.004103, Time: 0.12\n",
      "It: 6790, Loss: 4.279e-04, Lambda_1: 0.972, Lambda_2: 0.004103, Time: 0.11\n",
      "It: 6800, Loss: 2.464e-04, Lambda_1: 0.973, Lambda_2: 0.004102, Time: 0.12\n",
      "It: 6810, Loss: 5.392e-04, Lambda_1: 0.973, Lambda_2: 0.004102, Time: 0.14\n",
      "It: 6820, Loss: 2.685e-04, Lambda_1: 0.973, Lambda_2: 0.004102, Time: 0.13\n",
      "It: 6830, Loss: 5.857e-04, Lambda_1: 0.973, Lambda_2: 0.004102, Time: 0.12\n",
      "It: 6840, Loss: 3.502e-04, Lambda_1: 0.973, Lambda_2: 0.004101, Time: 0.11\n",
      "It: 6850, Loss: 5.283e-04, Lambda_1: 0.973, Lambda_2: 0.004101, Time: 0.11\n",
      "It: 6860, Loss: 4.836e-04, Lambda_1: 0.973, Lambda_2: 0.004101, Time: 0.11\n",
      "It: 6870, Loss: 3.595e-04, Lambda_1: 0.973, Lambda_2: 0.004101, Time: 0.12\n",
      "It: 6880, Loss: 5.226e-04, Lambda_1: 0.973, Lambda_2: 0.004100, Time: 0.13\n",
      "It: 6890, Loss: 2.694e-04, Lambda_1: 0.973, Lambda_2: 0.004100, Time: 0.12\n",
      "It: 6900, Loss: 2.821e-04, Lambda_1: 0.973, Lambda_2: 0.004100, Time: 0.11\n",
      "It: 6910, Loss: 3.612e-04, Lambda_1: 0.973, Lambda_2: 0.004100, Time: 0.11\n",
      "It: 6920, Loss: 2.872e-04, Lambda_1: 0.973, Lambda_2: 0.004100, Time: 0.11\n",
      "It: 6930, Loss: 5.789e-04, Lambda_1: 0.973, Lambda_2: 0.004100, Time: 0.11\n",
      "It: 6940, Loss: 2.408e-04, Lambda_1: 0.973, Lambda_2: 0.004099, Time: 0.11\n",
      "It: 6950, Loss: 3.185e-04, Lambda_1: 0.973, Lambda_2: 0.004099, Time: 0.11\n",
      "It: 6960, Loss: 3.826e-04, Lambda_1: 0.973, Lambda_2: 0.004099, Time: 0.13\n",
      "It: 6970, Loss: 2.127e-04, Lambda_1: 0.974, Lambda_2: 0.004099, Time: 0.13\n",
      "It: 6980, Loss: 6.114e-04, Lambda_1: 0.974, Lambda_2: 0.004099, Time: 0.11\n",
      "It: 6990, Loss: 2.617e-04, Lambda_1: 0.974, Lambda_2: 0.004098, Time: 0.11\n",
      "It: 7000, Loss: 4.745e-04, Lambda_1: 0.974, Lambda_2: 0.004098, Time: 0.11\n",
      "It: 7010, Loss: 2.720e-04, Lambda_1: 0.974, Lambda_2: 0.004098, Time: 0.12\n",
      "It: 7020, Loss: 2.795e-04, Lambda_1: 0.974, Lambda_2: 0.004098, Time: 0.13\n",
      "It: 7030, Loss: 5.090e-04, Lambda_1: 0.974, Lambda_2: 0.004098, Time: 0.11\n",
      "It: 7040, Loss: 3.626e-04, Lambda_1: 0.974, Lambda_2: 0.004097, Time: 0.11\n",
      "It: 7050, Loss: 4.695e-04, Lambda_1: 0.974, Lambda_2: 0.004097, Time: 0.11\n",
      "It: 7060, Loss: 3.880e-04, Lambda_1: 0.974, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 7070, Loss: 4.742e-04, Lambda_1: 0.974, Lambda_2: 0.004097, Time: 0.11\n",
      "It: 7080, Loss: 4.411e-04, Lambda_1: 0.974, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 7090, Loss: 2.816e-04, Lambda_1: 0.974, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 7100, Loss: 4.153e-04, Lambda_1: 0.974, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 7110, Loss: 2.210e-04, Lambda_1: 0.974, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 7120, Loss: 4.761e-04, Lambda_1: 0.975, Lambda_2: 0.004096, Time: 0.11\n",
      "It: 7130, Loss: 3.092e-04, Lambda_1: 0.975, Lambda_2: 0.004095, Time: 0.11\n",
      "It: 7140, Loss: 3.786e-04, Lambda_1: 0.975, Lambda_2: 0.004095, Time: 0.12\n",
      "It: 7150, Loss: 4.676e-04, Lambda_1: 0.975, Lambda_2: 0.004095, Time: 0.13\n",
      "It: 7160, Loss: 2.765e-04, Lambda_1: 0.975, Lambda_2: 0.004095, Time: 0.12\n",
      "It: 7170, Loss: 5.337e-04, Lambda_1: 0.975, Lambda_2: 0.004095, Time: 0.11\n",
      "It: 7180, Loss: 2.214e-04, Lambda_1: 0.975, Lambda_2: 0.004094, Time: 0.11\n",
      "It: 7190, Loss: 4.755e-04, Lambda_1: 0.975, Lambda_2: 0.004094, Time: 0.12\n",
      "It: 7200, Loss: 2.789e-04, Lambda_1: 0.975, Lambda_2: 0.004094, Time: 0.13\n",
      "It: 7210, Loss: 3.448e-04, Lambda_1: 0.975, Lambda_2: 0.004094, Time: 0.12\n",
      "It: 7220, Loss: 4.643e-04, Lambda_1: 0.975, Lambda_2: 0.004093, Time: 0.11\n",
      "It: 7230, Loss: 3.727e-04, Lambda_1: 0.975, Lambda_2: 0.004093, Time: 0.11\n",
      "It: 7240, Loss: 5.653e-04, Lambda_1: 0.975, Lambda_2: 0.004093, Time: 0.11\n",
      "It: 7250, Loss: 2.815e-04, Lambda_1: 0.975, Lambda_2: 0.004092, Time: 0.11\n",
      "It: 7260, Loss: 4.149e-04, Lambda_1: 0.975, Lambda_2: 0.004092, Time: 0.12\n",
      "It: 7270, Loss: 2.539e-04, Lambda_1: 0.975, Lambda_2: 0.004092, Time: 0.11\n",
      "It: 7280, Loss: 2.845e-04, Lambda_1: 0.975, Lambda_2: 0.004092, Time: 0.11\n",
      "It: 7290, Loss: 4.833e-04, Lambda_1: 0.976, Lambda_2: 0.004092, Time: 0.12\n",
      "It: 7300, Loss: 2.796e-04, Lambda_1: 0.976, Lambda_2: 0.004091, Time: 0.11\n",
      "It: 7310, Loss: 3.965e-04, Lambda_1: 0.976, Lambda_2: 0.004092, Time: 0.11\n",
      "It: 7320, Loss: 3.005e-04, Lambda_1: 0.976, Lambda_2: 0.004091, Time: 0.11\n",
      "It: 7330, Loss: 3.015e-04, Lambda_1: 0.976, Lambda_2: 0.004091, Time: 0.13\n",
      "It: 7340, Loss: 4.668e-04, Lambda_1: 0.976, Lambda_2: 0.004091, Time: 0.12\n",
      "It: 7350, Loss: 2.413e-04, Lambda_1: 0.976, Lambda_2: 0.004090, Time: 0.11\n",
      "It: 7360, Loss: 5.506e-04, Lambda_1: 0.976, Lambda_2: 0.004090, Time: 0.11\n",
      "It: 7370, Loss: 2.295e-04, Lambda_1: 0.976, Lambda_2: 0.004090, Time: 0.11\n",
      "It: 7380, Loss: 3.825e-04, Lambda_1: 0.976, Lambda_2: 0.004090, Time: 0.11\n",
      "It: 7390, Loss: 4.078e-04, Lambda_1: 0.976, Lambda_2: 0.004089, Time: 0.11\n",
      "It: 7400, Loss: 2.474e-04, Lambda_1: 0.976, Lambda_2: 0.004089, Time: 0.13\n",
      "It: 7410, Loss: 4.605e-04, Lambda_1: 0.976, Lambda_2: 0.004089, Time: 0.12\n",
      "It: 7420, Loss: 2.227e-04, Lambda_1: 0.976, Lambda_2: 0.004089, Time: 0.11\n",
      "It: 7430, Loss: 4.515e-04, Lambda_1: 0.976, Lambda_2: 0.004089, Time: 0.11\n",
      "It: 7440, Loss: 2.871e-04, Lambda_1: 0.976, Lambda_2: 0.004089, Time: 0.11\n",
      "It: 7450, Loss: 3.940e-04, Lambda_1: 0.976, Lambda_2: 0.004088, Time: 0.11\n",
      "It: 7460, Loss: 5.346e-04, Lambda_1: 0.977, Lambda_2: 0.004088, Time: 0.11\n",
      "It: 7470, Loss: 3.234e-04, Lambda_1: 0.977, Lambda_2: 0.004088, Time: 0.12\n",
      "It: 7480, Loss: 4.856e-04, Lambda_1: 0.977, Lambda_2: 0.004088, Time: 0.13\n",
      "It: 7490, Loss: 2.032e-04, Lambda_1: 0.977, Lambda_2: 0.004087, Time: 0.11\n",
      "It: 7500, Loss: 4.911e-04, Lambda_1: 0.977, Lambda_2: 0.004087, Time: 0.11\n",
      "It: 7510, Loss: 2.510e-04, Lambda_1: 0.977, Lambda_2: 0.004087, Time: 0.12\n",
      "It: 7520, Loss: 3.889e-04, Lambda_1: 0.977, Lambda_2: 0.004087, Time: 0.12\n",
      "It: 7530, Loss: 3.785e-04, Lambda_1: 0.977, Lambda_2: 0.004086, Time: 0.13\n",
      "It: 7540, Loss: 2.946e-04, Lambda_1: 0.977, Lambda_2: 0.004086, Time: 0.13\n",
      "It: 7550, Loss: 5.307e-04, Lambda_1: 0.977, Lambda_2: 0.004086, Time: 0.12\n",
      "It: 7560, Loss: 2.603e-04, Lambda_1: 0.977, Lambda_2: 0.004086, Time: 0.12\n",
      "It: 7570, Loss: 4.069e-04, Lambda_1: 0.977, Lambda_2: 0.004085, Time: 0.13\n",
      "It: 7580, Loss: 2.656e-04, Lambda_1: 0.977, Lambda_2: 0.004085, Time: 0.13\n",
      "It: 7590, Loss: 2.988e-04, Lambda_1: 0.977, Lambda_2: 0.004085, Time: 0.12\n",
      "It: 7600, Loss: 4.602e-04, Lambda_1: 0.977, Lambda_2: 0.004085, Time: 0.12\n",
      "It: 7610, Loss: 3.131e-04, Lambda_1: 0.977, Lambda_2: 0.004084, Time: 0.12\n",
      "It: 7620, Loss: 4.060e-04, Lambda_1: 0.977, Lambda_2: 0.004085, Time: 0.11\n",
      "It: 7630, Loss: 2.814e-04, Lambda_1: 0.977, Lambda_2: 0.004084, Time: 0.12\n",
      "It: 7640, Loss: 3.083e-04, Lambda_1: 0.977, Lambda_2: 0.004084, Time: 0.11\n",
      "It: 7650, Loss: 4.458e-04, Lambda_1: 0.977, Lambda_2: 0.004084, Time: 0.11\n",
      "It: 7660, Loss: 2.461e-04, Lambda_1: 0.977, Lambda_2: 0.004084, Time: 0.12\n",
      "It: 7670, Loss: 4.345e-04, Lambda_1: 0.978, Lambda_2: 0.004083, Time: 0.13\n",
      "It: 7680, Loss: 2.144e-04, Lambda_1: 0.978, Lambda_2: 0.004083, Time: 0.12\n",
      "It: 7690, Loss: 3.891e-04, Lambda_1: 0.978, Lambda_2: 0.004083, Time: 0.12\n",
      "It: 7700, Loss: 3.226e-04, Lambda_1: 0.978, Lambda_2: 0.004083, Time: 0.11\n",
      "It: 7710, Loss: 3.221e-04, Lambda_1: 0.978, Lambda_2: 0.004082, Time: 0.12\n",
      "It: 7720, Loss: 4.276e-04, Lambda_1: 0.978, Lambda_2: 0.004082, Time: 0.13\n",
      "It: 7730, Loss: 2.404e-04, Lambda_1: 0.978, Lambda_2: 0.004082, Time: 0.13\n",
      "It: 7740, Loss: 5.105e-04, Lambda_1: 0.978, Lambda_2: 0.004082, Time: 0.11\n",
      "It: 7750, Loss: 2.194e-04, Lambda_1: 0.978, Lambda_2: 0.004081, Time: 0.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 7760, Loss: 3.737e-04, Lambda_1: 0.978, Lambda_2: 0.004081, Time: 0.12\n",
      "It: 7770, Loss: 4.015e-04, Lambda_1: 0.978, Lambda_2: 0.004081, Time: 0.13\n",
      "It: 7780, Loss: 2.979e-04, Lambda_1: 0.978, Lambda_2: 0.004081, Time: 0.12\n",
      "It: 7790, Loss: 5.367e-04, Lambda_1: 0.978, Lambda_2: 0.004081, Time: 0.12\n",
      "It: 7800, Loss: 2.324e-04, Lambda_1: 0.978, Lambda_2: 0.004080, Time: 0.12\n",
      "It: 7810, Loss: 4.773e-04, Lambda_1: 0.978, Lambda_2: 0.004080, Time: 0.12\n",
      "It: 7820, Loss: 2.238e-04, Lambda_1: 0.978, Lambda_2: 0.004080, Time: 0.12\n",
      "It: 7830, Loss: 3.212e-04, Lambda_1: 0.978, Lambda_2: 0.004080, Time: 0.11\n",
      "It: 7840, Loss: 4.495e-04, Lambda_1: 0.978, Lambda_2: 0.004079, Time: 0.13\n",
      "It: 7850, Loss: 3.124e-04, Lambda_1: 0.978, Lambda_2: 0.004079, Time: 0.12\n",
      "It: 7860, Loss: 4.921e-04, Lambda_1: 0.978, Lambda_2: 0.004079, Time: 0.12\n",
      "It: 7870, Loss: 1.961e-04, Lambda_1: 0.978, Lambda_2: 0.004079, Time: 0.12\n",
      "It: 7880, Loss: 4.257e-04, Lambda_1: 0.978, Lambda_2: 0.004078, Time: 0.12\n",
      "It: 7890, Loss: 2.449e-04, Lambda_1: 0.979, Lambda_2: 0.004078, Time: 0.12\n",
      "It: 7900, Loss: 5.136e-04, Lambda_1: 0.978, Lambda_2: 0.004078, Time: 0.11\n",
      "It: 7910, Loss: 2.852e-04, Lambda_1: 0.979, Lambda_2: 0.004078, Time: 0.11\n",
      "It: 7920, Loss: 2.593e-04, Lambda_1: 0.979, Lambda_2: 0.004077, Time: 0.13\n",
      "It: 7930, Loss: 5.053e-04, Lambda_1: 0.979, Lambda_2: 0.004078, Time: 0.13\n",
      "It: 7940, Loss: 2.468e-04, Lambda_1: 0.979, Lambda_2: 0.004077, Time: 0.12\n",
      "It: 7950, Loss: 3.557e-04, Lambda_1: 0.979, Lambda_2: 0.004077, Time: 0.11\n",
      "It: 7960, Loss: 2.643e-04, Lambda_1: 0.979, Lambda_2: 0.004077, Time: 0.12\n",
      "It: 7970, Loss: 2.800e-04, Lambda_1: 0.979, Lambda_2: 0.004077, Time: 0.12\n",
      "It: 7980, Loss: 4.275e-04, Lambda_1: 0.979, Lambda_2: 0.004076, Time: 0.11\n",
      "It: 7990, Loss: 2.954e-04, Lambda_1: 0.979, Lambda_2: 0.004076, Time: 0.12\n",
      "It: 8000, Loss: 3.688e-04, Lambda_1: 0.979, Lambda_2: 0.004076, Time: 0.12\n",
      "It: 8010, Loss: 2.253e-04, Lambda_1: 0.979, Lambda_2: 0.004076, Time: 0.12\n",
      "It: 8020, Loss: 3.646e-04, Lambda_1: 0.979, Lambda_2: 0.004075, Time: 0.12\n",
      "It: 8030, Loss: 3.395e-04, Lambda_1: 0.979, Lambda_2: 0.004075, Time: 0.12\n",
      "It: 8040, Loss: 3.013e-04, Lambda_1: 0.979, Lambda_2: 0.004075, Time: 0.12\n",
      "It: 8050, Loss: 4.088e-04, Lambda_1: 0.979, Lambda_2: 0.004074, Time: 0.12\n",
      "It: 8060, Loss: 2.230e-04, Lambda_1: 0.979, Lambda_2: 0.004074, Time: 0.12\n",
      "It: 8070, Loss: 3.738e-04, Lambda_1: 0.979, Lambda_2: 0.004074, Time: 0.12\n",
      "It: 8080, Loss: 2.022e-04, Lambda_1: 0.979, Lambda_2: 0.004074, Time: 0.12\n",
      "It: 8090, Loss: 3.697e-04, Lambda_1: 0.979, Lambda_2: 0.004074, Time: 0.12\n",
      "It: 8100, Loss: 4.165e-04, Lambda_1: 0.979, Lambda_2: 0.004074, Time: 0.12\n",
      "It: 8110, Loss: 2.872e-04, Lambda_1: 0.979, Lambda_2: 0.004073, Time: 0.14\n",
      "It: 8120, Loss: 5.575e-04, Lambda_1: 0.980, Lambda_2: 0.004073, Time: 0.13\n",
      "It: 8130, Loss: 2.445e-04, Lambda_1: 0.980, Lambda_2: 0.004073, Time: 0.12\n",
      "It: 8140, Loss: 4.880e-04, Lambda_1: 0.979, Lambda_2: 0.004073, Time: 0.12\n",
      "It: 8150, Loss: 1.936e-04, Lambda_1: 0.980, Lambda_2: 0.004072, Time: 0.13\n",
      "It: 8160, Loss: 4.181e-04, Lambda_1: 0.980, Lambda_2: 0.004072, Time: 0.12\n",
      "It: 8170, Loss: 2.571e-04, Lambda_1: 0.980, Lambda_2: 0.004072, Time: 0.12\n",
      "It: 8180, Loss: 2.983e-04, Lambda_1: 0.980, Lambda_2: 0.004072, Time: 0.12\n",
      "It: 8190, Loss: 4.066e-04, Lambda_1: 0.980, Lambda_2: 0.004071, Time: 0.12\n",
      "It: 8200, Loss: 3.009e-04, Lambda_1: 0.980, Lambda_2: 0.004071, Time: 0.13\n",
      "It: 8210, Loss: 4.225e-04, Lambda_1: 0.980, Lambda_2: 0.004071, Time: 0.13\n",
      "It: 8220, Loss: 3.393e-04, Lambda_1: 0.980, Lambda_2: 0.004071, Time: 0.12\n",
      "It: 8230, Loss: 3.656e-04, Lambda_1: 0.980, Lambda_2: 0.004070, Time: 0.13\n",
      "It: 8240, Loss: 2.716e-04, Lambda_1: 0.980, Lambda_2: 0.004070, Time: 0.12\n",
      "It: 8250, Loss: 2.026e-04, Lambda_1: 0.980, Lambda_2: 0.004070, Time: 0.12\n",
      "It: 8260, Loss: 4.522e-04, Lambda_1: 0.980, Lambda_2: 0.004070, Time: 0.11\n",
      "It: 8270, Loss: 2.404e-04, Lambda_1: 0.980, Lambda_2: 0.004070, Time: 0.12\n",
      "It: 8280, Loss: 4.218e-04, Lambda_1: 0.980, Lambda_2: 0.004070, Time: 0.12\n",
      "It: 8290, Loss: 3.168e-04, Lambda_1: 0.980, Lambda_2: 0.004069, Time: 0.12\n",
      "It: 8300, Loss: 3.147e-04, Lambda_1: 0.980, Lambda_2: 0.004069, Time: 0.12\n",
      "It: 8310, Loss: 3.255e-04, Lambda_1: 0.980, Lambda_2: 0.004069, Time: 0.12\n",
      "It: 8320, Loss: 1.890e-04, Lambda_1: 0.980, Lambda_2: 0.004069, Time: 0.11\n",
      "It: 8330, Loss: 3.800e-04, Lambda_1: 0.980, Lambda_2: 0.004068, Time: 0.12\n",
      "It: 8340, Loss: 2.548e-04, Lambda_1: 0.980, Lambda_2: 0.004068, Time: 0.12\n",
      "It: 8350, Loss: 3.818e-04, Lambda_1: 0.980, Lambda_2: 0.004068, Time: 0.12\n",
      "It: 8360, Loss: 4.774e-04, Lambda_1: 0.980, Lambda_2: 0.004067, Time: 0.12\n",
      "It: 8370, Loss: 4.185e-04, Lambda_1: 0.980, Lambda_2: 0.004067, Time: 0.12\n",
      "It: 8380, Loss: 5.323e-04, Lambda_1: 0.980, Lambda_2: 0.004067, Time: 0.12\n",
      "It: 8390, Loss: 1.879e-04, Lambda_1: 0.980, Lambda_2: 0.004067, Time: 0.12\n",
      "It: 8400, Loss: 5.742e-04, Lambda_1: 0.980, Lambda_2: 0.004066, Time: 0.12\n",
      "It: 8410, Loss: 1.901e-04, Lambda_1: 0.980, Lambda_2: 0.004066, Time: 0.11\n",
      "It: 8420, Loss: 2.591e-04, Lambda_1: 0.980, Lambda_2: 0.004066, Time: 0.11\n",
      "It: 8430, Loss: 3.188e-04, Lambda_1: 0.981, Lambda_2: 0.004066, Time: 0.12\n",
      "It: 8440, Loss: 1.881e-04, Lambda_1: 0.981, Lambda_2: 0.004066, Time: 0.12\n",
      "It: 8450, Loss: 4.697e-04, Lambda_1: 0.981, Lambda_2: 0.004066, Time: 0.12\n",
      "It: 8460, Loss: 2.086e-04, Lambda_1: 0.981, Lambda_2: 0.004065, Time: 0.11\n",
      "It: 8470, Loss: 4.309e-04, Lambda_1: 0.981, Lambda_2: 0.004065, Time: 0.12\n",
      "It: 8480, Loss: 2.031e-04, Lambda_1: 0.981, Lambda_2: 0.004065, Time: 0.11\n",
      "It: 8490, Loss: 2.757e-04, Lambda_1: 0.981, Lambda_2: 0.004065, Time: 0.11\n",
      "It: 8500, Loss: 3.933e-04, Lambda_1: 0.981, Lambda_2: 0.004064, Time: 0.11\n",
      "It: 8510, Loss: 2.323e-04, Lambda_1: 0.981, Lambda_2: 0.004064, Time: 0.12\n",
      "It: 8520, Loss: 3.420e-04, Lambda_1: 0.981, Lambda_2: 0.004064, Time: 0.11\n",
      "It: 8530, Loss: 2.133e-04, Lambda_1: 0.981, Lambda_2: 0.004064, Time: 0.12\n",
      "It: 8540, Loss: 4.172e-04, Lambda_1: 0.981, Lambda_2: 0.004063, Time: 0.12\n",
      "It: 8550, Loss: 2.424e-04, Lambda_1: 0.981, Lambda_2: 0.004063, Time: 0.12\n",
      "It: 8560, Loss: 3.781e-04, Lambda_1: 0.981, Lambda_2: 0.004063, Time: 0.12\n",
      "It: 8570, Loss: 3.824e-04, Lambda_1: 0.981, Lambda_2: 0.004063, Time: 0.12\n",
      "It: 8580, Loss: 2.967e-04, Lambda_1: 0.981, Lambda_2: 0.004062, Time: 0.11\n",
      "It: 8590, Loss: 4.491e-04, Lambda_1: 0.981, Lambda_2: 0.004063, Time: 0.11\n",
      "It: 8600, Loss: 2.486e-04, Lambda_1: 0.981, Lambda_2: 0.004062, Time: 0.12\n",
      "It: 8610, Loss: 4.224e-04, Lambda_1: 0.981, Lambda_2: 0.004062, Time: 0.13\n",
      "It: 8620, Loss: 2.487e-04, Lambda_1: 0.981, Lambda_2: 0.004061, Time: 0.12\n",
      "It: 8630, Loss: 2.241e-04, Lambda_1: 0.981, Lambda_2: 0.004061, Time: 0.12\n",
      "It: 8640, Loss: 6.171e-04, Lambda_1: 0.981, Lambda_2: 0.004061, Time: 0.12\n",
      "It: 8650, Loss: 2.393e-04, Lambda_1: 0.981, Lambda_2: 0.004061, Time: 0.12\n",
      "It: 8660, Loss: 3.008e-04, Lambda_1: 0.981, Lambda_2: 0.004061, Time: 0.12\n",
      "It: 8670, Loss: 1.734e-04, Lambda_1: 0.981, Lambda_2: 0.004060, Time: 0.12\n",
      "It: 8680, Loss: 1.750e-04, Lambda_1: 0.981, Lambda_2: 0.004060, Time: 0.12\n",
      "It: 8690, Loss: 2.129e-04, Lambda_1: 0.981, Lambda_2: 0.004060, Time: 0.12\n",
      "It: 8700, Loss: 2.126e-04, Lambda_1: 0.981, Lambda_2: 0.004060, Time: 0.12\n",
      "It: 8710, Loss: 1.767e-04, Lambda_1: 0.981, Lambda_2: 0.004060, Time: 0.12\n",
      "It: 8720, Loss: 1.537e-04, Lambda_1: 0.982, Lambda_2: 0.004060, Time: 0.12\n",
      "It: 8730, Loss: 1.759e-04, Lambda_1: 0.982, Lambda_2: 0.004059, Time: 0.11\n",
      "It: 8740, Loss: 1.871e-04, Lambda_1: 0.982, Lambda_2: 0.004059, Time: 0.12\n",
      "It: 8750, Loss: 1.546e-04, Lambda_1: 0.982, Lambda_2: 0.004059, Time: 0.12\n",
      "It: 8760, Loss: 2.318e-04, Lambda_1: 0.982, Lambda_2: 0.004059, Time: 0.11\n",
      "It: 8770, Loss: 1.698e-04, Lambda_1: 0.982, Lambda_2: 0.004058, Time: 0.12\n",
      "It: 8780, Loss: 3.210e-04, Lambda_1: 0.982, Lambda_2: 0.004058, Time: 0.11\n",
      "It: 8790, Loss: 1.877e-04, Lambda_1: 0.982, Lambda_2: 0.004058, Time: 0.12\n",
      "It: 8800, Loss: 1.753e-04, Lambda_1: 0.982, Lambda_2: 0.004058, Time: 0.12\n",
      "It: 8810, Loss: 2.579e-04, Lambda_1: 0.982, Lambda_2: 0.004057, Time: 0.11\n",
      "It: 8820, Loss: 2.725e-04, Lambda_1: 0.982, Lambda_2: 0.004057, Time: 0.11\n",
      "It: 8830, Loss: 3.005e-04, Lambda_1: 0.982, Lambda_2: 0.004057, Time: 0.11\n",
      "It: 8840, Loss: 8.283e-04, Lambda_1: 0.982, Lambda_2: 0.004057, Time: 0.12\n",
      "It: 8850, Loss: 5.584e-04, Lambda_1: 0.982, Lambda_2: 0.004056, Time: 0.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 8860, Loss: 8.596e-03, Lambda_1: 0.982, Lambda_2: 0.004056, Time: 0.12\n",
      "It: 8870, Loss: 4.649e-01, Lambda_1: 0.977, Lambda_2: 0.004032, Time: 0.12\n",
      "It: 8880, Loss: 3.631e-01, Lambda_1: 0.987, Lambda_2: 0.003990, Time: 0.12\n",
      "It: 8890, Loss: 2.554e-01, Lambda_1: 1.000, Lambda_2: 0.003947, Time: 0.12\n",
      "It: 8900, Loss: 1.861e-01, Lambda_1: 1.007, Lambda_2: 0.003926, Time: 0.12\n",
      "It: 8910, Loss: 1.185e-01, Lambda_1: 1.011, Lambda_2: 0.003920, Time: 0.12\n",
      "It: 8920, Loss: 5.578e-02, Lambda_1: 1.010, Lambda_2: 0.003920, Time: 0.11\n",
      "It: 8930, Loss: 2.793e-02, Lambda_1: 1.004, Lambda_2: 0.003932, Time: 0.11\n",
      "It: 8940, Loss: 2.208e-02, Lambda_1: 0.997, Lambda_2: 0.003947, Time: 0.12\n",
      "It: 8950, Loss: 1.402e-02, Lambda_1: 0.990, Lambda_2: 0.003959, Time: 0.11\n",
      "It: 8960, Loss: 1.044e-02, Lambda_1: 0.984, Lambda_2: 0.003973, Time: 0.12\n",
      "It: 8970, Loss: 7.968e-03, Lambda_1: 0.978, Lambda_2: 0.003982, Time: 0.12\n",
      "It: 8980, Loss: 6.333e-03, Lambda_1: 0.974, Lambda_2: 0.003990, Time: 0.11\n",
      "It: 8990, Loss: 5.235e-03, Lambda_1: 0.970, Lambda_2: 0.003996, Time: 0.12\n",
      "It: 9000, Loss: 4.424e-03, Lambda_1: 0.967, Lambda_2: 0.004002, Time: 0.11\n",
      "It: 9010, Loss: 3.804e-03, Lambda_1: 0.964, Lambda_2: 0.004006, Time: 0.12\n",
      "It: 9020, Loss: 3.318e-03, Lambda_1: 0.962, Lambda_2: 0.004009, Time: 0.12\n",
      "It: 9030, Loss: 2.924e-03, Lambda_1: 0.960, Lambda_2: 0.004012, Time: 0.12\n",
      "It: 9040, Loss: 2.600e-03, Lambda_1: 0.958, Lambda_2: 0.004015, Time: 0.12\n",
      "It: 9050, Loss: 2.330e-03, Lambda_1: 0.956, Lambda_2: 0.004017, Time: 0.12\n",
      "It: 9060, Loss: 2.105e-03, Lambda_1: 0.955, Lambda_2: 0.004019, Time: 0.11\n",
      "It: 9070, Loss: 1.917e-03, Lambda_1: 0.953, Lambda_2: 0.004021, Time: 0.11\n",
      "It: 9080, Loss: 1.759e-03, Lambda_1: 0.952, Lambda_2: 0.004023, Time: 0.12\n",
      "It: 9090, Loss: 1.627e-03, Lambda_1: 0.951, Lambda_2: 0.004024, Time: 0.12\n",
      "It: 9100, Loss: 1.516e-03, Lambda_1: 0.950, Lambda_2: 0.004025, Time: 0.11\n",
      "It: 9110, Loss: 1.423e-03, Lambda_1: 0.949, Lambda_2: 0.004026, Time: 0.11\n",
      "It: 9120, Loss: 1.342e-03, Lambda_1: 0.949, Lambda_2: 0.004027, Time: 0.11\n",
      "It: 9130, Loss: 1.273e-03, Lambda_1: 0.948, Lambda_2: 0.004028, Time: 0.12\n",
      "It: 9140, Loss: 1.213e-03, Lambda_1: 0.947, Lambda_2: 0.004029, Time: 0.11\n",
      "It: 9150, Loss: 1.161e-03, Lambda_1: 0.946, Lambda_2: 0.004030, Time: 0.12\n",
      "It: 9160, Loss: 1.113e-03, Lambda_1: 0.946, Lambda_2: 0.004030, Time: 0.11\n",
      "It: 9170, Loss: 1.071e-03, Lambda_1: 0.945, Lambda_2: 0.004031, Time: 0.12\n",
      "It: 9180, Loss: 1.033e-03, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9190, Loss: 9.983e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.12\n",
      "It: 9200, Loss: 9.664e-04, Lambda_1: 0.944, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9210, Loss: 9.370e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.11\n",
      "It: 9220, Loss: 9.099e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9230, Loss: 8.848e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.11\n",
      "It: 9240, Loss: 8.614e-04, Lambda_1: 0.943, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9250, Loss: 8.396e-04, Lambda_1: 0.943, Lambda_2: 0.004034, Time: 0.11\n",
      "It: 9260, Loss: 8.192e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9270, Loss: 8.001e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.11\n",
      "It: 9280, Loss: 7.823e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9290, Loss: 7.654e-04, Lambda_1: 0.942, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9300, Loss: 7.496e-04, Lambda_1: 0.942, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9310, Loss: 7.346e-04, Lambda_1: 0.942, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9320, Loss: 7.204e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9330, Loss: 7.070e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9340, Loss: 6.943e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9350, Loss: 6.821e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9360, Loss: 6.706e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9370, Loss: 6.595e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9380, Loss: 6.490e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9390, Loss: 6.388e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9400, Loss: 6.291e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9410, Loss: 6.198e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9420, Loss: 6.108e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9430, Loss: 6.022e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9440, Loss: 5.938e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9450, Loss: 5.858e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9460, Loss: 5.780e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9470, Loss: 5.704e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9480, Loss: 5.631e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9490, Loss: 5.560e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9500, Loss: 5.491e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9510, Loss: 5.424e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9520, Loss: 5.359e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9530, Loss: 5.295e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9540, Loss: 5.233e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9550, Loss: 5.173e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.12\n",
      "It: 9560, Loss: 5.114e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9570, Loss: 5.057e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9580, Loss: 5.000e-04, Lambda_1: 0.941, Lambda_2: 0.004035, Time: 0.11\n",
      "It: 9590, Loss: 4.946e-04, Lambda_1: 0.941, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9600, Loss: 4.892e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9610, Loss: 4.839e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9620, Loss: 4.788e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9630, Loss: 4.737e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.11\n",
      "It: 9640, Loss: 4.688e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9650, Loss: 4.639e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9660, Loss: 4.592e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9670, Loss: 4.545e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9680, Loss: 4.499e-04, Lambda_1: 0.942, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9690, Loss: 4.454e-04, Lambda_1: 0.943, Lambda_2: 0.004034, Time: 0.11\n",
      "It: 9700, Loss: 4.410e-04, Lambda_1: 0.943, Lambda_2: 0.004034, Time: 0.12\n",
      "It: 9710, Loss: 4.366e-04, Lambda_1: 0.943, Lambda_2: 0.004034, Time: 0.11\n",
      "It: 9720, Loss: 4.323e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9730, Loss: 4.281e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9740, Loss: 4.240e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9750, Loss: 4.199e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.11\n",
      "It: 9760, Loss: 4.159e-04, Lambda_1: 0.943, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9770, Loss: 4.119e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.11\n",
      "It: 9780, Loss: 4.080e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9790, Loss: 4.042e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9800, Loss: 4.004e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9810, Loss: 3.967e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.12\n",
      "It: 9820, Loss: 3.930e-04, Lambda_1: 0.944, Lambda_2: 0.004033, Time: 0.11\n",
      "It: 9830, Loss: 3.894e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9840, Loss: 3.859e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.12\n",
      "It: 9850, Loss: 3.823e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9860, Loss: 3.789e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.12\n",
      "It: 9870, Loss: 3.755e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.12\n",
      "It: 9880, Loss: 3.721e-04, Lambda_1: 0.945, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9890, Loss: 3.688e-04, Lambda_1: 0.946, Lambda_2: 0.004032, Time: 0.12\n",
      "It: 9900, Loss: 3.655e-04, Lambda_1: 0.946, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9910, Loss: 3.623e-04, Lambda_1: 0.946, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9920, Loss: 3.591e-04, Lambda_1: 0.946, Lambda_2: 0.004032, Time: 0.11\n",
      "It: 9930, Loss: 3.560e-04, Lambda_1: 0.946, Lambda_2: 0.004031, Time: 0.11\n",
      "It: 9940, Loss: 3.529e-04, Lambda_1: 0.946, Lambda_2: 0.004031, Time: 0.11\n",
      "It: 9950, Loss: 3.498e-04, Lambda_1: 0.947, Lambda_2: 0.004031, Time: 0.12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It: 9960, Loss: 3.468e-04, Lambda_1: 0.947, Lambda_2: 0.004031, Time: 0.12\n",
      "It: 9970, Loss: 3.438e-04, Lambda_1: 0.947, Lambda_2: 0.004031, Time: 0.12\n",
      "It: 9980, Loss: 3.409e-04, Lambda_1: 0.947, Lambda_2: 0.004031, Time: 0.11\n",
      "It: 9990, Loss: 3.380e-04, Lambda_1: 0.947, Lambda_2: 0.004031, Time: 0.12\n",
      "Loss: 3.354379e-04, l1: 0.94751, l2: 0.00403\n",
      "Loss: 9.393351e+00, l1: 1.50155, l2: 0.00381\n",
      "Loss: 4.321905e-02, l1: 1.00641, l2: 0.00401\n",
      "Loss: 3.351714e-04, l1: 0.94764, l2: 0.00403\n",
      "Loss: 3.346692e-04, l1: 0.94786, l2: 0.00403\n",
      "Loss: 3.333429e-04, l1: 0.94886, l2: 0.00403\n",
      "Loss: 3.321051e-04, l1: 0.94957, l2: 0.00403\n",
      "Loss: 3.309626e-04, l1: 0.95005, l2: 0.00403\n",
      "Loss: 3.284797e-04, l1: 0.95130, l2: 0.00403\n",
      "Loss: 3.262255e-04, l1: 0.95268, l2: 0.00403\n",
      "Loss: 3.233330e-04, l1: 0.95450, l2: 0.00403\n",
      "Loss: 3.198515e-04, l1: 0.95666, l2: 0.00403\n",
      "Loss: 3.161650e-04, l1: 0.95917, l2: 0.00403\n",
      "Loss: 3.128075e-04, l1: 0.96040, l2: 0.00403\n",
      "Loss: 3.092876e-04, l1: 0.96147, l2: 0.00403\n",
      "Loss: 3.062407e-04, l1: 0.96262, l2: 0.00403\n",
      "Loss: 3.040106e-04, l1: 0.96359, l2: 0.00403\n",
      "Loss: 3.008677e-04, l1: 0.96515, l2: 0.00403\n",
      "Loss: 2.946329e-04, l1: 0.96818, l2: 0.00404\n",
      "Loss: 2.851085e-04, l1: 0.97235, l2: 0.00404\n",
      "Loss: 2.738356e-04, l1: 0.97648, l2: 0.00404\n",
      "Loss: 2.660187e-04, l1: 0.97842, l2: 0.00405\n",
      "Loss: 2.604114e-04, l1: 0.97838, l2: 0.00405\n",
      "Loss: 2.571966e-04, l1: 0.97872, l2: 0.00405\n",
      "Loss: 2.549250e-04, l1: 0.97796, l2: 0.00405\n",
      "Loss: 2.525172e-04, l1: 0.97908, l2: 0.00405\n",
      "Loss: 2.508488e-04, l1: 0.97926, l2: 0.00405\n",
      "Loss: 2.482100e-04, l1: 0.98064, l2: 0.00405\n",
      "Loss: 2.448582e-04, l1: 0.98194, l2: 0.00406\n",
      "Loss: 2.402628e-04, l1: 0.98336, l2: 0.00406\n",
      "Loss: 2.369903e-04, l1: 0.98354, l2: 0.00406\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
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    {
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     "output_type": "stream",
     "text": [
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     ]
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Loss: 3.802648e-05, l1: 0.99841, l2: 0.00317\n",
      "INFO:tensorflow:Optimization terminated with:\n",
      "  Message: b'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
      "  Objective function value: 0.000038\n",
      "  Number of iterations: 2314\n",
      "  Number of functions evaluations: 2479\n",
      "Error lambda_1: 0.159115%\n",
      "Error lambda_2: 0.261760%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "nu = 0.01/np.pi\n",
    "\n",
    "N_u = 2000\n",
    "layers = [2, 20, 20, 20, 20, 20, 20, 20, 20, 1]\n",
    "\n",
    "data = scipy.io.loadmat('Data/burgers_shock.mat')\n",
    "\n",
    "t = data['t'].flatten()[:,None]\n",
    "x = data['x'].flatten()[:,None]\n",
    "Exact = np.real(data['usol']).T\n",
    "\n",
    "X, T = np.meshgrid(x,t)\n",
    "\n",
    "X_star = np.hstack((X.flatten()[:,None], T.flatten()[:,None]))\n",
    "u_star = Exact.flatten()[:,None]              \n",
    "\n",
    "# Doman bounds\n",
    "lb = X_star.min(0)\n",
    "ub = X_star.max(0)    \n",
    "\n",
    "######################################################################\n",
    "######################## Noiseles Data ###############################\n",
    "######################################################################\n",
    "noise = 0.0            \n",
    "\n",
    "idx = np.random.choice(X_star.shape[0], N_u, replace=False)\n",
    "X_u_train = X_star[idx,:]\n",
    "u_train = u_star[idx,:]\n",
    "\n",
    "model = PhysicsInformedNN(X_u_train, u_train, layers, lb, ub)\n",
    "model.train(0)\n",
    "\n",
    "u_pred, f_pred = model.predict(X_star)\n",
    "\n",
    "error_u = np.linalg.norm(u_star-u_pred,2)/np.linalg.norm(u_star,2)\n",
    "\n",
    "U_pred = griddata(X_star, u_pred.flatten(), (X, T), method='cubic')\n",
    "\n",
    "lambda_1_value = model.sess.run(model.lambda_1)\n",
    "lambda_2_value = model.sess.run(model.lambda_2)\n",
    "lambda_2_value = np.exp(lambda_2_value)\n",
    "\n",
    "error_lambda_1 = np.abs(lambda_1_value - 1.0)*100\n",
    "error_lambda_2 = np.abs(lambda_2_value - nu)/nu * 100\n",
    "\n",
    "print('Error u: %e' % (error_u))    \n",
    "print('Error l1: %.5f%%' % (error_lambda_1))                             \n",
    "print('Error l2: %.5f%%' % (error_lambda_2))  \n",
    "\n",
    "######################################################################\n",
    "########################### Noisy Data ###############################\n",
    "######################################################################\n",
    "noise = 0.01        \n",
    "u_train = u_train + noise*np.std(u_train)*np.random.randn(u_train.shape[0], u_train.shape[1])\n",
    "\n",
    "model = PhysicsInformedNN(X_u_train, u_train, layers, lb, ub)\n",
    "model.train(10000)\n",
    "\n",
    "u_pred, f_pred = model.predict(X_star)\n",
    "\n",
    "lambda_1_value_noisy = model.sess.run(model.lambda_1)\n",
    "lambda_2_value_noisy = model.sess.run(model.lambda_2)\n",
    "lambda_2_value_noisy = np.exp(lambda_2_value_noisy)\n",
    "\n",
    "error_lambda_1_noisy = np.abs(lambda_1_value_noisy - 1.0)*100\n",
    "error_lambda_2_noisy = np.abs(lambda_2_value_noisy - nu)/nu * 100\n",
    "\n",
    "print('Error lambda_1: %f%%' % (error_lambda_1_noisy))\n",
    "print('Error lambda_2: %f%%' % (error_lambda_2_noisy))                           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######################################################################\n",
    "############################# Plotting ###############################\n",
    "######################################################################    \n",
    "\n",
    "fig = plt.figure(figsize=(12,10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.axis('off')\n",
    "\n",
    "####### Row 0: u(t,x) ##################    \n",
    "gs0 = gridspec.GridSpec(1, 2)\n",
    "gs0.update(top=1-0.06, bottom=1-1.0/3.0+0.06, left=0.15, right=0.85, wspace=0)\n",
    "ax = plt.subplot(gs0[:, :])\n",
    "\n",
    "h = ax.imshow(U_pred.T, interpolation='nearest', cmap='rainbow', \n",
    "              extent=[t.min(), t.max(), x.min(), x.max()], \n",
    "              origin='lower', aspect='auto')\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "fig.colorbar(h, cax=cax)\n",
    "\n",
    "ax.plot(X_u_train[:,1], X_u_train[:,0], 'kx', label = 'Data (%d points)' % (u_train.shape[0]), markersize = 2, clip_on = False)\n",
    "\n",
    "line = np.linspace(x.min(), x.max(), 2)[:,None]\n",
    "ax.plot(t[25]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
    "ax.plot(t[50]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
    "ax.plot(t[75]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
    "\n",
    "ax.set_xlabel('$t$')\n",
    "ax.set_ylabel('$x$')\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.0, -0.125), ncol=5, frameon=False)\n",
    "ax.set_title('$u(t,x)$', fontsize = 10)\n",
    "\n",
    "####### Row 1: u(t,x) slices ##################    \n",
    "gs1 = gridspec.GridSpec(1, 3)\n",
    "gs1.update(top=1-1.0/3.0-0.1, bottom=1.0-2.0/3.0, left=0.1, right=0.9, wspace=0.5)\n",
    "\n",
    "ax = plt.subplot(gs1[0, 0])\n",
    "ax.plot(x,Exact[25,:], 'b-', linewidth = 2, label = 'Exact')       \n",
    "ax.plot(x,U_pred[25,:], 'r--', linewidth = 2, label = 'Prediction')\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$u(t,x)$')    \n",
    "ax.set_title('$t = 0.25$', fontsize = 10)\n",
    "ax.axis('square')\n",
    "ax.set_xlim([-1.1,1.1])\n",
    "ax.set_ylim([-1.1,1.1])\n",
    "\n",
    "ax = plt.subplot(gs1[0, 1])\n",
    "ax.plot(x,Exact[50,:], 'b-', linewidth = 2, label = 'Exact')       \n",
    "ax.plot(x,U_pred[50,:], 'r--', linewidth = 2, label = 'Prediction')\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$u(t,x)$')\n",
    "ax.axis('square')\n",
    "ax.set_xlim([-1.1,1.1])\n",
    "ax.set_ylim([-1.1,1.1])\n",
    "ax.set_title('$t = 0.50$', fontsize = 10)\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.35), ncol=5, frameon=False)\n",
    "\n",
    "ax = plt.subplot(gs1[0, 2])\n",
    "ax.plot(x,Exact[75,:], 'b-', linewidth = 2, label = 'Exact')       \n",
    "ax.plot(x,U_pred[75,:], 'r--', linewidth = 2, label = 'Prediction')\n",
    "ax.set_xlabel('$x$')\n",
    "ax.set_ylabel('$u(t,x)$')\n",
    "ax.axis('square')\n",
    "ax.set_xlim([-1.1,1.1])\n",
    "ax.set_ylim([-1.1,1.1])    \n",
    "ax.set_title('$t = 0.75$', fontsize = 10)\n",
    "\n",
    "####### Row 3: Identified PDE ##################    \n",
    "gs2 = gridspec.GridSpec(1, 3)\n",
    "gs2.update(top=1.0-2.0/3.0, bottom=0, left=0.0, right=1.0, wspace=0.0)\n",
    "\n",
    "ax = plt.subplot(gs2[:, :])\n",
    "ax.axis('off')\n",
    "\n",
    "# s1 = r'$\\begin{tabular}{ |c|c| }  \\hline Correct PDE & $u_t + u u_x - 0.0031831 u_{xx} = 0$ \\\\  \\hline Identified PDE (clean data) & '\n",
    "# s2 = r'$u_t + %.5f u u_x - %.7f u_{xx} = 0$ \\\\  \\hline ' % (lambda_1_value, lambda_2_value)\n",
    "# s3 = r'Identified PDE (1\\% noise) & '\n",
    "# s4 = r'$u_t + %.5f u u_x - %.7f u_{xx} = 0$  \\\\  \\hline ' % (lambda_1_value_noisy, lambda_2_value_noisy)\n",
    "# s5 = r'\\end{tabular}$'\n",
    "# s = s1+s2+s3+s4+s5\n",
    "\n",
    "\n",
    "s = r\"\"\"\n",
    "Correct    PDE: $u_t + u u_x - 0.0031831 u_{xx} = 0$\n",
    "Identified PDE: (clean data) $u_t + 0.99816 u u_x - 0.0032027 u_{xx} = 0$\n",
    "Identified PDE: (1% noise) $u_t + 0.99841 u u_x - 0.0031748 u_{xx} = 0$  \n",
    "\"\"\"\n",
    "ax.text(0.1,0.1,s)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试 matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ax=plt.subplot(111)\n",
    "ax.text(0.1,0.8,r\"$\\int_a^b f(x)\\mathrm{d}x$\",fontsize=30,color=\"red\")\n",
    "ax.text(0.1,0.3,r\"$\\sum_{n=1}^\\infty\\frac{-e^{i\\pi}}{2^n}!$\",fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.6",
   "language": "python",
   "name": "py3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
